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timestamp[us]date 2025-04-09 16:14:38
2025-04-09 16:14:38
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timestamp[us]date 2025-04-09 16:14:38
2025-05-26 14:27:41
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531264b9-ed7e-4e80-acde-defd72b78cf3
|
completed
| 2025-04-09T16:14:38.079197 | 2025-05-26T08:03:58.880079 |
0845658c-dc87-41d3-9811-b337d608522a
|
Short-term, automated car rental services, i.e., car sharing, are a solution that has been improving in urban transportation systems over the past few years. Due to the intensive expansion of the systems, service providers face increasing challenges in their competitiveness. One of them is to meet the customer expectations for the fleet of vehicles offered in the system. Although this aspect is noted primarily in the literature review on fleet optimization and management, there is a gap in research on the appropriate selection of vehicle models. In response, the article aimed to identify the vehicles best suited for car-sharing systems from the customer’s point of view. The selection of suitable vehicles was treated as a multi-criteria decision-making issue; therefore, the study used ELECTRE III—one of the multi-criteria decision-making methods. The work focuses on researching the opinions of users who rarely use car-sharing services in Poland. The most popular car models in 2021, equipped with internal combustion, hybrid, and electric engines, were selected for the analysis. The results indicate that the best suited cars are relatively large, spacious, and equipped with electric drive and represent the D segment of vehicles in Europe. In addition, these vehicles are to be equipped with a powerful engine, a spacious boot, and a fast battery charging time. Interestingly, small city cars, so far associated with car sharing, ranked the worst in the classification method. In addition, factors such as the warranty period associated with the quality of the vehicles, or the number of car doors, are not very important to users. The results support car-sharing operators in the process of selecting or modernizing a fleet of vehicles.
|
<li> <b>car:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>car:</b> Car<li> <b>cars:</b> Car
|
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[
null
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{
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},
{
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"label": "vehicleType",
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}
] | null | null |
7b0a8d3c-5055-47de-a1c6-0cc9a29e4ab9
|
completed
| 2025-04-09T16:14:38.079210 | 2025-05-26T08:49:36.651807 |
a073ee97-a8c5-4dce-9361-42d7d8eac25c
|
Minutes needed to top up fuel/electricity to maximum fuel tank capacity or car battery capacity.
|
<li> <b>car:</b> Car
|
[
[
{
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"label": "vehicleType",
"start": 75
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 78,
"label": "vehicleType",
"start": 75
}
] | null | null |
1c7912e7-ddfd-495f-bc40-3c574f8f3c47
|
completed
| 2025-04-09T16:14:38.079217 | 2025-05-26T13:47:23.030611 |
18edd7f4-074f-4d32-9a40-32bae2f53bca
|
A smarter transport system that caters for social, economic and environmental sustainability is arguably one of the most critical prerequisites for creating pathways to more livable urban futures. This paper aims to provide a state-of-the-art analysis of a selection of mobility initiatives that may dictate the future of urban transportation and make cities smarter. These are mechanisms either recently introduced with encouraging uptake so far and much greater potential to contribute in a shift to a better transport paradigm or still in an embryonic stage of their development and yet to be embraced as powerful mechanisms that could change travel behaviour norms. Autonomous and connected vehicles are set to revolutionise the urban landscape by allowing machines to take over driving that for over a century has been exclusively a human activity, while electrical vehicles are already helping decarbonising the transport sector. Bus rapid transit has been steadily reinventing and rebranding conventional bus services revitalising the use of the humblest form of public transport, while hyperloop is an entirely new, disruptive, and somewhat provocative, travel mode proposition based on the use of sealed tube systems through which pods could travel free of air resistance with speeds exceeding 1000 km/h. Shared use mobility mechanisms like car-sharing, ride-sharing, ride-sourcing and public bicycles can help establishing a culture for using mobility resources on an as-needed basis, while mobility-as-a-service will take this sharing culture a step further, offering tailored mobility and trip planning packages that could entirely replace the need for privately owned modes of transport.
|
<li> <b>vehicles:</b> Other Vehicle<li> <b>Bus:</b> Bus<li> <b>bus:</b> Bus<li> <b>bicycles:</b> Cyclist
|
[
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{
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},
{
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"label": "vehicleType",
"start": 1012
},
{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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[
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] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
"Correct"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
{
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{
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{
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"start": 1012
},
{
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"label": "VRUType",
"start": 1402
}
] | null | null |
f4ba3bb0-001f-4f00-8e5e-788f6ed00e7f
|
completed
| 2025-04-09T16:14:38.079223 | 2025-05-26T08:34:06.769699 |
17f9e1f1-de46-4bfa-ab01-d836424d5657
|
Ride-sharing (or carpooling) refers to a mode of transportation in which individual travellers share a vehicle for a trip and split travel costs such as gas, toll, and parking fees with others that have similar itineraries and time schedules [70].This sharing approach has an immediate and potentially easily measurable impact on mobility patterns since if three potential drivers, people that are not susceptible to shift to another mode of transportation, decide to share a ride this means that only one car will be used instead of three.In theory, ride-sharing is a system which combines the flexibility and speed of private cars with the reduced cost of fixed-line systems and is directly battling the negative externalities of single occupant car travel, which is the most unsustainable form of travel behaviour.Ride-sharing is relevant, and if presented in a potent way could be also particularly attractive, for commuters that want to go to work in a cost-effective and flexible way; there are many employers that promote and organise ride-sharing programmes for their staff.Advantages of ride-sharing for participants (both drivers and passengers), to society, and to the environment include saving travel costs, reducing travel time, mitigating traffic congestion, conserving fuel, and reducing air pollution [71,72]. Today, dedicated platforms allow drivers to post their rides online helping to mitigate many issues, which previously limited ride-sharing.These digital platforms help by establishing trust among strangers through rating and review systems, meaningful profiles, user verification, and automated booking and payment processes and by dramatically decreasing transactional cost for ride listing and search [73].These technological advancements, which will only continue to improve as IoT evolves and real-time monitoring and live matching capabilities become better, have already enabled the establishment of large ride-sharing initiatives like RelayRides, BlaBlaCar, or Carpooling.com that facilitate millions of trips per day.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>car:</b> Car<li> <b>cars:</b> Car
|
[
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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[
"Correct"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
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[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
{
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{
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{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 628
}
] | null | null |
54e6e90e-2598-4a2e-84ec-27590d8c9bc3
|
completed
| 2025-04-09T16:14:38.079229 | 2025-05-23T21:39:26.002300 |
16cd1f80-e386-4351-9d69-653cc8140222
|
Industrial and transport activities are the two major sources of noise pollution in any metropolitan city. Lucknow city, the capital of the largest populated state Uttar Pradesh in India has an area of 310 sq. km and is rapidly growing as a commercial, industrial and trading centre of northern India. The population of Lucknow city as per census 2001 is 22.45 Lacs. It is expected that by the year 2021 it will make 45 Lacs. The total vehicle population in Lucknow city on 31 March 2008, was nearly 1 million with almost 80% two wheelers, 12% cars, 1.36% three wheelers, 0.45% buses etc. A study was carried out to assess the existing status of noise levels and its impacts on the environment with a possibility of further expansion of the city. Ambient noise levels were measured at different locations selected on the basis of land use such as silence, heavy traffic and residential and commercial zones. It was found that noise levels at all selected locations were much higher (75–90 dB) than the prescribed limits. The observed traffic volume and data on road geometry were used to predict noise levels using Federal Highway Administration Agency (FHWA) model and the calculated noise levels were compared with the observed levels for checking the suitability of this model for predicting the future levels. It was established that the results obtained by FHWA model were very close to the observed noise levels and that the model was suitable to be used for other similar metropolitan cities in India.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>two wheelers:</b> Motorcycle<li> <b>cars:</b> Car<li> <b>buses:</b> Bus
|
[
[
{
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{
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{
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{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 556
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"three wheelers\" should be identified as vehicleType, exactly like \"two wheelers\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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},
{
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},
{
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}
] | null | null |
926357f3-e504-49d9-9263-55f3ef10bdd0
|
completed
| 2025-04-09T16:14:38.079235 | 2025-05-20T14:44:01.170848 |
9099a45b-5e7a-4cd2-a047-819543563d1b
|
Road traffi c noise in most of the urban areas is increasing at an alarming rate which is a cause of concern for the residents living along the highways.Noise levels must be controlled in order to reduce its societal impacts.Reducing noise levels may be produced following Harris (1979) who suggested some noise control measures like: • motor vehicle control; • land use control; • highway planning and design; • buff er zones; • noise barriers; • using dead end streets for residential complexes; • depressing freeways and arterial roads below the ground level; • creating more gap between road and buildings; • constructing high rise buildings along the roads providing barrier for low rise buildings; • making external and internal sound insulated walls; • making double glazed windows.
|
<li> <b>motor vehicle:</b> Other Vehicle
|
[
[
{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 350,
"label": "vehicleType",
"start": 337
}
] | null | null |
bc6c7d0b-4cbd-493e-af9b-72fc45e2fffb
|
completed
| 2025-04-09T16:14:38.079241 | 2025-05-19T11:49:58.834604 |
9668b511-cce8-4ebf-9b83-5e13ca2b0e5b
|
The work was dedicated to the subject of innovative autonomous vehicles on the transport market. The paper presents basic information about autonomous cars: a nomenclature characteristic of autonomous vehicles, along with the terms “automatic”, “autonomous”, “self-drive” and “driverless”. The article also presents various types of autonomous cars based on the most popular classifications in the world. The purpose of the work is to present basic issues related to autonomous vehicles.
|
<li> <b>vehicles:</b> Other Vehicle<li> <b>cars:</b> Car
|
[
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{
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},
{
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{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"We added the combined terms \"autonomous vehicle\" & \"autonomous car\" in the vehicleType category"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 201
},
{
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},
{
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"label": "vehicleType",
"start": 151
},
{
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"label": "vehicleType",
"start": 344
}
] | null | null |
0a1b98f5-1a75-4399-ae79-58055ccbfbf0
|
completed
| 2025-04-09T16:14:38.079247 | 2025-05-13T06:13:29.949379 |
6edeeeac-e005-418a-afa1-fdbdcc27a9b7
|
Autonomous driving levels according to the NHTSA classification Level Description 0 The driver's task is to support all systems in the vehicle. 1 The vehicle is equipped with automatic versions of some systems (e.g., ESP systems, ABS, automatic braking), which can be activated spontaneously or with the help of the driver.However, the driver's task is to oversee system functioning.
|
<li> <b>0:</b> Level 0<li> <b>vehicle:</b> Other Vehicle<li> <b>1:</b> Level 1
|
[
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{
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},
{
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},
{
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"label": "vehicleType",
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},
{
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"label": "levelOfAutomation",
"start": 144
},
{
"end": 25,
"label": "levelOfAutomation",
"start": 0
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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"start": 135
},
{
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"start": 150
},
{
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"start": 144
}
] | null | null |
2245515a-b795-4d2f-92e1-859e046df4e4
|
completed
| 2025-04-09T16:14:38.079253 | 2025-05-26T08:38:31.190222 |
b886e51a-2d88-4dad-86bd-716325a1c7b5
|
This article explored the development of electric vehicle (EV) charging stations in Thailand between 2015 and 2020. This research aimed to study the main players and examine their goals, strategies, and operations in the EV charging business as well as the key issues that these charging operators have encountered in developing charging stations. The authors collected qualitative data (direct interviews with managers, video interviews, news, research articles, industry reports and press releases of EV charging operators) and used a constant comparison approach to analyze the data. The study found that after 2015, the Thai government created technology-push policies to kick-start the investment in the EV charging station business (such as subsidies for charging stations, setting a temporary selling price for electricity and building an EV charging consortium). The main players in the Thai charging business include: (1) oil and gas companies; (2) electricity state enterprises; (3) green energy companies; (4) start-ups; and (5) automotive companies. The goals of investing in the charging business for the oil and gas incumbents were to find a new growth engine and to prepare for the potential disruption in the energy sector whereas the green energy companies and start-ups wanted to capture customer bases in this promising industry. These players tended to use a partnership strategy to expand charging networks at key locations (malls, restaurants, offices). Regarding the key issues in expanding the EV charging network, the operators suggested that the high upfront investment costs, small number of EV users, and the high electricity prices (from the demand charge and usage guarantee fee) make them ‘wait-and-see’ and cautiously expand the charging network. Finally, we found that the government tried to address the constraints by setting up a national EV policy committee to accelerate EV adoption and EV charging stations in Thailand. The committee also set a fixed and reduced electricity price for charging operators.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>EV:</b> Car
|
[
[
{
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"label": "vehicleType",
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{
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{
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{
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},
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},
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{
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},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
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},
{
"end": 1911,
"label": "vehicleType",
"start": 1909
},
{
"end": 1927,
"label": "vehicleType",
"start": 1925
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EV\" should be recognized as \"other vehicle\" and not as \"car\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 57,
"label": "vehicleType",
"start": 50
},
{
"end": 61,
"label": "vehicleType",
"start": 59
},
{
"end": 223,
"label": "vehicleType",
"start": 221
},
{
"end": 505,
"label": "vehicleType",
"start": 503
},
{
"end": 711,
"label": "vehicleType",
"start": 709
},
{
"end": 848,
"label": "vehicleType",
"start": 846
},
{
"end": 1520,
"label": "vehicleType",
"start": 1518
},
{
"end": 1621,
"label": "vehicleType",
"start": 1619
},
{
"end": 1877,
"label": "vehicleType",
"start": 1875
},
{
"end": 1911,
"label": "vehicleType",
"start": 1909
},
{
"end": 1927,
"label": "vehicleType",
"start": 1925
}
] | null | null |
e37e2604-a12a-4834-acfe-5351f9f6e86a
|
completed
| 2025-04-09T16:14:38.079260 | 2025-05-26T14:13:09.674408 |
15c75685-f0c3-43a6-9c77-35cdd8e6f256
|
Along with the tax incentives (see Table 1) for the investment in the EV industry in Thailand, the Board of Investment of Thailand provides five-year corporate tax exemption for the EV charging station business.The imported machinery and related raw materials used in the charging stations will also receive import-tax exemptions.In 2018, Energy Mahanakhon Co., Ltd. was granted tax privileges by the Board of Investment to build 3000 EV charging stations all over the country [65].
|
<li> <b>EV:</b> Car
|
[
[
{
"end": 72,
"label": "vehicleType",
"start": 70
},
{
"end": 184,
"label": "vehicleType",
"start": 182
},
{
"end": 437,
"label": "vehicleType",
"start": 435
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" could be other vehicles than cars"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 72,
"label": "vehicleType",
"start": 70
},
{
"end": 184,
"label": "vehicleType",
"start": 182
},
{
"end": 437,
"label": "vehicleType",
"start": 435
}
] | null | null |
23512198-38a6-412a-800a-be469f84d622
|
completed
| 2025-04-09T16:14:38.079266 | 2025-05-26T06:53:51.729028 |
558228e0-4031-4473-be2b-cd3aeee6ba01
|
This paper explores the factors affecting the uptake of hybrid and electric vehicles in the European Union (EU) using data from two extensive cross-sectional surveys. Each survey consists of 26,500 responses to a questionnaire that combines socio-economic and behavioral aspects. The share of respondents across the EU stating that they would certainly or probably consider purchasing a hybrid or a battery-powered electric vehicle (H&EV) in the near future rose from 32% in 2014 to 37.4% in 2018. There is, however, a high variability among EU member states, as well as across different socio-economic groups. Propensity is highly correlated with income, educational attainment, and urbanization level. In order to address the high degree of collinearity, we applied a machine learning classification model to analyze and explain the interaction between the variables that affected the expressed propensity to purchase such a vehicle. The findings highlight something largely missing from the literature, namely that local conditions and regional variation are a major, if not decisive, factor regarding purchasing choices. Seen from a policy perspective, this conclusion may provide guidance regarding how to support the take up of H&EVs through measures that are tailored to the specific needs at the local level.
|
<li> <b>hybrid:</b> Car<li> <b>electric vehicles:</b> Car<li> <b>hybrid:</b> Car<li> <b>electric vehicle:</b> Car<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 387
},
{
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"label": "vehicleType",
"start": 67
},
{
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"label": "vehicleType",
"start": 415
},
{
"end": 934,
"label": "vehicleType",
"start": 927
},
{
"end": 437,
"label": "vehicleType",
"start": 433
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"electric vehicle could be a two- or three- wheeler"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 62,
"label": "vehicleType",
"start": 56
},
{
"end": 393,
"label": "vehicleType",
"start": 387
},
{
"end": 84,
"label": "vehicleType",
"start": 67
},
{
"end": 62,
"label": "vehicleType",
"start": 56
},
{
"end": 393,
"label": "vehicleType",
"start": 387
},
{
"end": 431,
"label": "vehicleType",
"start": 415
},
{
"end": 431,
"label": "vehicleType",
"start": 424
},
{
"end": 934,
"label": "vehicleType",
"start": 927
}
] | null | null |
99186771-ee47-450c-973d-20c9b7fa6a04
|
completed
| 2025-04-09T16:14:38.079272 | 2025-05-26T14:24:05.605765 |
b3e81fdb-a5fb-429d-b196-fb662631585c
|
Our approach was based on a classification model that uses the results of two cross-sectional surveys, carried out across all 28 EU member states in 2014 [35] and 2018 [36].The model predicts whether a respondent is likely to have stated a high propensity to purchase a hybrid or electric car using the respondent's answers to the other survey questions as independent variables.Since most of the survey questions correspond to socio-economic or behavioral variables, a robust predictive model allows for the identification of the variables that affect this choice the most and can help policymakers and other stakeholders in improving their strategies to increase the market share of new car technologies. We used the gradient boosting method, a machine learning technique with numerous applications in predictive modelling [37].The applications of machine learning classifiers in transport are rapidly increasing.For example, Choi and Ahn [38] conducted a multi-level analysis combining individual and urban characteristics regarding model trip preferences, Focas and Christidis [39] analyzed car use, while Cheng et al. [40] developed a mode choice model using a random forest method.Machine learning approaches allow for a higher model precision in modelling user choices than conventional stated preference methods [41]. The main advantages of using a data driven, tree-based approach, as opposed to other discrete choice methods frequently used in the literature (multinomial logit, hybrid choice, or latent class models), are the ability to account for the co-dependence between independent variables and the absence of strict assumptions concerning the model structure and parameters.Moreover, in the particular case of the surveys used here, gradient boosting classifiers allow for the use of categorical variables without assuming proportional odds, i.e., constant distances or monotonous relationships between their values.Machine learning approaches offer a wide range of metrics to estimate model precision, as well as tools for the interpretation of the results.A potential disadvantage of such approaches stems from the nature of the issue to be analyzed: the large number of non-linear tree-like decisions may obscure the physical interpretation of the resulting model [42,43].Additionally, unless a suitable validation strategy is in place, models that overfit with respect to their underlying data may not generalize well when applied to new data. The dependent variable of our model was a binary variable (0/1) indicating whether the user had a high propensity to purchase a hybrid or electric vehicle (value = 1) or not (value = 0).The binary variable was a direct transformation of the original multiclass response variable in order to simplify the model and concentrate on the two responses of main interest (high propensity corresponds to "certainly yes" or "probably yes").The modelling setup followed a 40-40-20% random split of the dataset into training, testing, and validation sets, respectively.This a standard practice in machine learning predictive modelling.The model was trained on the training set and its precision was evaluated using the testing set.The validation set was used for the final evaluation to ensure that the model did not overfit and that it could generalize sufficiently well to previously unseen data.As an evaluation metric, we used AUC (area under the curve), the most frequently used aggregate measure of performance for classification models.AUC is a function of the true positive and false positive rates of the model results and can range from 0 (when predictions are 100% wrong) to 1 (when all predictions are correct).Regarding feature engineering, we created additional variables based on the two-way interaction of categorical variables.The categorical variables, original and engineered, were subsequently transformed into numeric variables using the standard mean likelihood encoding technique (probability of the dependent variable, conditional on each category value). We constructed two different base models using the variables included in the year 2014 survey (model A) and the year 2018 survey (model B) respectively.The difference between the A and B models was the inclusion in the 2018 survey of four questions concerning Information and Communication Technologies (ICT) use and solutions, online-shopping, and teleworking, which were not present in the 2014 survey, plus the resulting two-way interaction variables. The train-test-validation procedure was performed in three combinations: All three models achieved a satisfactory level of precision, explaining a considerable part of the variance in user preferences.There were obviously additional factors that influenced the responses and were not covered by the surveys used, but the overall model performance was sufficient for conclusions on certain aspects to be drawn.The stability of the validation set AUC when using different years in model A (0.789 with 2014 data, 0.791 with 2018 data) suggests that the model could be generalized using different datasets and that the relative importance of its variables remained constant between the two points in time. Likelihood by age and income played an important role in all three models, while the importance of the region's level of education and urbanization declined between 2014 and 2018.The average likelihood at a regional level and at a center/suburb level remained equally important between time points.The importance of gender, already relatively low in 2014, decreased further.The change in the importance of the variables suggests that while the adopter profile remained centered on specific age and income groups and was still dependent on the local conditions of the respondents' region and urbanization level (Section 3 and Figures 567), the concept of H&EVs has become more familiar to respondents in other socio-economic groups.As a result, the variation in the propensity to purchase appeared to decrease between 2014 and 2018. The considerable improvement between model A and model B when using the year 2018 data (AUC from 0.791 to 0.821) could be attributed to the inclusion of the four new questions in the year 2018 survey, which allowed the model to capture additional information that could enrich the interpretation of certain behavioral aspects.The number of variables that were considered as important (i.e., contribute to variance) were 115 in model A. Adding the year 2018 new survey questions and their derived variables increased the number of important variables to 164 in model B. Figures 123show the twelve variables with the highest relative importance of the boosted decision trees within each model, ranked in terms of the contribution of each variable to the model.Variable names that include "likelihood" were derived from the mean likelihood encoding (MLE) of category combinations.
|
<li> <b>hybrid or electric car:</b> Car<li> <b>car:</b> Car<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
"end": 292,
"label": "vehicleType",
"start": 270
},
{
"end": 292,
"label": "vehicleType",
"start": 289
},
{
"end": 692,
"label": "vehicleType",
"start": 689
},
{
"end": 1098,
"label": "vehicleType",
"start": 1095
},
{
"end": 2620,
"label": "vehicleType",
"start": 2613
},
{
"end": 2620,
"label": "vehicleType",
"start": 2604
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 292,
"label": "vehicleType",
"start": 270
},
{
"end": 292,
"label": "vehicleType",
"start": 289
},
{
"end": 692,
"label": "vehicleType",
"start": 689
},
{
"end": 1098,
"label": "vehicleType",
"start": 1095
},
{
"end": 2620,
"label": "vehicleType",
"start": 2613
}
] | null | null |
37ffef40-d0ee-4a9c-8dad-ea01e3764120
|
completed
| 2025-04-09T16:14:38.079279 | 2025-05-19T11:46:55.207974 |
8b936570-bf59-4384-8188-eba371003999
|
Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods.
|
<li> <b>Autonomous Underwater Vehicle (AUV):</b> Other Vehicle<li> <b>AUV:</b> Other Vehicle<li> <b>Inertial Navigation System (INS):</b> Other Sensor<li> <b>Doppler Velocity Log (DVL):</b> Other Sensor<li> <b>Sonar:</b> Other Sensor
|
[
[
{
"end": 1321,
"label": "sensorType",
"start": 1289
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"AUVs are not of interest in CCAM (we are mostly interested in ground Autonomous Vehicles)\nSonars and DVLs are not of interest in CCAM\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 159,
"label": "vehicleType",
"start": 124
},
{
"end": 158,
"label": "vehicleType",
"start": 155
},
{
"end": 575,
"label": "vehicleType",
"start": 572
},
{
"end": 831,
"label": "vehicleType",
"start": 828
},
{
"end": 1003,
"label": "vehicleType",
"start": 1000
},
{
"end": 1263,
"label": "vehicleType",
"start": 1260
},
{
"end": 1321,
"label": "sensorType",
"start": 1289
},
{
"end": 1349,
"label": "sensorType",
"start": 1323
},
{
"end": 1372,
"label": "sensorType",
"start": 1367
}
] | null | null |
39c826e4-4cd7-4dc4-973a-ce4e81cc31a3
|
completed
| 2025-04-09T16:14:38.079285 | 2025-05-26T07:09:24.247733 |
ecbd48c6-1247-4daa-8337-c0438fb46972
|
When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.
|
<li> <b>underwater vehicles:</b> Other Vehicle
|
[
[
{
"end": 24,
"label": "vehicleType",
"start": 16
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"underwater vehicles\" are not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 24,
"label": "vehicleType",
"start": 5
}
] | null | null |
03ebd117-f54a-4d09-92e0-7bd0bf013833
|
completed
| 2025-04-09T16:14:38.079291 | 2025-05-26T14:06:06.089492 |
e177b65c-3621-4dc6-bebb-0255356121bc
|
Underwater image enhancement algorithms have high requirements for engineering applications.FUnIE-GAN has a memory requirement of 17 MB and runs at 25.4 frames per second on an embedded platform (Nvidia Jetson TX2), and 148.5 frames per second on a graphics card (Nvidia GTX 1080).The running speed on the robot CPU (Intel Core-I3 6100U) is 7.9 frames.The algorithm FUnIE-GAN-NIQE proposed in this paper retains the advantage of a fast calculation speed of FUnIE-GAN, and can be fully applied to the real-time underwater image enhancement process of an underwater robot.
|
<li> <b>robot:</b> Other Vehicle<li> <b>underwater robot:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"robot\" should not be recognized as vehicleType\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 311,
"label": "vehicleType",
"start": 306
},
{
"end": 569,
"label": "vehicleType",
"start": 564
},
{
"end": 569,
"label": "vehicleType",
"start": 553
}
] | null | null |
d57fd9b1-adeb-4432-970d-9ec4882d6b12
|
completed
| 2025-04-09T16:14:38.079297 | 2025-05-26T08:49:25.025845 |
7eae8975-983e-4fcd-bf9d-1c14fad96a03
|
The energy transition, a process in which fossil fuels are being replaced by cleaner sources of energy, comes with many challenges. The intrinsic uncertainty associated with renewable energy sources has led to a search for complementary technologies to tackle those issues. In recent years, the use of electric vehicles (EVs) has been studied as an alternative for storage, leading to a much more complex market structure. Small participants are now willing to provide energy, helping to keep the desired balance of supply and demand. In this paper, we analyse the electricity spot market, providing a model where EVs decide to participate depending on the underlying conditions. We study pricing rules adapted from versions currently in use in electricity markets, and focus on two of them for our experimental settings: integer programming (IP) and extended locational marginal (ELM) pricing. We particularly pay attention to the properties those prices might satisfy, and numerically test them under some scenarios representing different levels of participation of EVs and an active demand side. Our results suggest that IP pricing generally derives larger individual uplift payments and further produces public prices that are not well aligned with the final payments of market participants, leading to distortions in the market.
|
<li> <b>electric vehicles (EVs):</b> Car<li> <b>EVs:</b> Car
|
[
[
{
"end": 325,
"label": "vehicleType",
"start": 302
},
{
"end": 617,
"label": "vehicleType",
"start": 614
},
{
"end": 1071,
"label": "vehicleType",
"start": 1068
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" are not cars exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 325,
"label": "vehicleType",
"start": 302
},
{
"end": 324,
"label": "vehicleType",
"start": 321
},
{
"end": 617,
"label": "vehicleType",
"start": 614
},
{
"end": 1071,
"label": "vehicleType",
"start": 1068
}
] | null | null |
42803fb7-09ae-41a7-80fe-97885d0f2519
|
completed
| 2025-04-09T16:14:38.079303 | 2025-05-26T14:00:04.540537 |
bf9c9a80-5b13-4843-9050-88a905d07ad3
|
The bidding language for EVs essentially takes into account technical restrictions for the state of charge and the (dis-)charging quantities.The state of charge for each EV v and period t is denoted by the decision variable soc v,t (soc v,t ≥ 0).Likewise, the charging quantity of EV v in period t is defined by q + v,t (q + v,t ≥ 0), and the discharging quantity by q - v,t (q - v,t ≥ 0).Given the capacity requirements SOC v,t (SOC v,t ≥ 0) and SOC v,t (SOC v,t ≥ 0), the electricity demand for driving ∆SOC v,t (∆SOC v,t ≥ 0), as well as the (dis-)charging efficiencies η + (0 ≤ η + ≤ 1) and η -(0 ≤ η -≤ 1), the state of charge is defined by the following equations: Constraints (2e) and (2f) formulate the state of charge as a function of scheduled (dis-)charge plus unscheduled power demand (i.e., driving).By Equation (2g), it is assumed that the vehicles are fully charged at the end of each day.(This condition can be easily changed.We designed in this way to fit in the experimental setting.An alternative is to consider them fully charged before departure.)Constraint (2h) establishes that the state of charge is limited by the minimum and maximum capacity requirements of the vehicle's battery. For the (dis-)charging processes, a limit on the maximum (dis-)charged power ≥ 0) must be maintained.Note that the parameter has a value of zero when EV v is unplugged in period t.Furthermore, it is only possible to either charge or discharge an EV v in period t.This binary decision is denoted by the variable m v,t (m v,t ∈ {0, 1}), which equals one if EV v is charging in period t and zero if it is discharging.M is a parameter described by the following equations:
|
<li> <b>EVs:</b> Car<li> <b>EV:</b> Car<li> <b>vehicles:</b> Other Vehicle
|
[
[
{
"end": 28,
"label": "vehicleType",
"start": 25
},
{
"end": 172,
"label": "vehicleType",
"start": 170
},
{
"end": 283,
"label": "vehicleType",
"start": 281
},
{
"end": 1359,
"label": "vehicleType",
"start": 1357
},
{
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"label": "vehicleType",
"start": 1453
},
{
"end": 1564,
"label": "vehicleType",
"start": 1562
},
{
"end": 862,
"label": "vehicleType",
"start": 854
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" could be other vehicles besides cars"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 28,
"label": "vehicleType",
"start": 25
},
{
"end": 172,
"label": "vehicleType",
"start": 170
},
{
"end": 283,
"label": "vehicleType",
"start": 281
},
{
"end": 1359,
"label": "vehicleType",
"start": 1357
},
{
"end": 1455,
"label": "vehicleType",
"start": 1453
},
{
"end": 1564,
"label": "vehicleType",
"start": 1562
},
{
"end": 862,
"label": "vehicleType",
"start": 854
}
] | null | null |
22f3f408-6fdf-4b12-801a-a1e2468f07d8
|
completed
| 2025-04-09T16:14:38.079309 | 2025-05-26T07:44:46.111768 |
83dbf05c-5cb4-409b-8ec2-7bf5e10983ce
|
Building a sustainable and eco-friendly transport system is crucial to tackling global challenges such as climate change, as transport can be seen as one of the main sources of air pollution and CO2 emissions in megacities, particularly in developing countries. By bringing together multiple modes of travel, and combining different transport provider options into a single service, Mobility-as-a-Service (MaaS) could offer an effective way to help build a sustainable city by improving public transport services. However, the strategies used to develop MaaS, vary in different cities based on their specific multimodal transport facilities and service. Many residents of Beijing have to contend with long-distance commuting, which may adversely affect individuals’ travel experience and satisfaction and is, therefore, a key issue for transport development in Beijing. Using Beijing as a case study, we carried out in-depth interviews and thereby captured long-distance commuters’ concerns and needs concerning their commuting experiences. Our findings show that long-distance commuters are primarily concerned about the following multimodal commuting scenarios: “Underground + bicycle”, “Underground + taxi”, “Underground + private car”, and “Underground + bus”. Therefore, we suggest that the priority should be to develop a MaaS model for Beijing that focuses on the integration of multimodal transport connected to the underground rail system.
|
<li> <b>bicycle:</b> Cyclist<li> <b>taxi:</b> Car<li> <b>private car:</b> Car<li> <b>bus:</b> Bus
|
[
[
{
"end": 1186,
"label": "VRUType",
"start": 1179
},
{
"end": 1208,
"label": "vehicleType",
"start": 1204
},
{
"end": 1237,
"label": "vehicleType",
"start": 1226
},
{
"end": 1262,
"label": "vehicleType",
"start": 1259
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 1186,
"label": "VRUType",
"start": 1179
},
{
"end": 1208,
"label": "vehicleType",
"start": 1204
},
{
"end": 1237,
"label": "vehicleType",
"start": 1226
},
{
"end": 1262,
"label": "vehicleType",
"start": 1259
}
] | null | null |
1888ed57-f7b2-4acf-83d7-40d4b43560b2
|
completed
| 2025-04-09T16:14:38.079315 | 2025-05-13T05:54:57.773098 |
dbc13623-f6a0-4c5f-9819-82633124663c
|
However, those commuters who live outside the fifth ring but without cars, always rely on car-hailing or taxis for work in Beijing.Yet, during peaking hours, it is very hard to get a taxi.Under such concerns, MaaS can dispatch the taxis in advance through the realization of integrated planning.For example, after being informed of the departure and arrival time of the subway as well as the flow of commuters, MaaS can instruct taxi drivers to prepare ahead of time at the station.It is a win-win strategy for both travellers and the environment.As for the commuters can receive better and smoother service in less time.As for the environment, by reducing the time for taxis to drive empty while waiting for the orders, the implementation of MaaS can thus decrease Carbon emissions, which is rather eco-friendly.
|
<li> <b>cars:</b> Car<li> <b>taxis:</b> Car<li> <b>taxi:</b> Car
|
[
[
{
"end": 73,
"label": "vehicleType",
"start": 69
},
{
"end": 110,
"label": "vehicleType",
"start": 105
},
{
"end": 236,
"label": "vehicleType",
"start": 231
},
{
"end": 675,
"label": "vehicleType",
"start": 670
},
{
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"label": "vehicleType",
"start": 183
},
{
"end": 433,
"label": "vehicleType",
"start": 429
},
{
"end": 93,
"label": "vehicleType",
"start": 90
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Car-hailing: \"car\" was not identified as entity\nTaxi: could be also a \"van\"/\"mini van\", but we agree that \"car\" is the prevalent equivalent\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 73,
"label": "vehicleType",
"start": 69
},
{
"end": 110,
"label": "vehicleType",
"start": 105
},
{
"end": 236,
"label": "vehicleType",
"start": 231
},
{
"end": 675,
"label": "vehicleType",
"start": 670
},
{
"end": 187,
"label": "vehicleType",
"start": 183
},
{
"end": 433,
"label": "vehicleType",
"start": 429
}
] | null | null |
e4039302-9b44-4a4d-8ce8-ebea2f1a5bf8
|
completed
| 2025-04-09T16:14:38.079321 | 2025-05-20T11:01:16.807014 |
883bf43c-4639-49f4-8132-6d970b1a46c7
|
Purpose. The purpose of work is the possibility estimation of аthermic technologies use of cold-deformed metal softening for elements of railway car body and wheel. Methodology. The material for research is the carbon steel of the wheel rim fragment containing 0.55%С, 0.74%Mn, 0.33%Si, and the steel 20. The wheel steel is studied after heat strengthening and cold work after operation. Steel 20 is studied after plastic cold work by rolling. Electric pulse treatment (ET) is carried out on the special equipment. As the property of metal strength the Vickers hardness number is used. The microstructure research is carried out using the light and electronic microscope. Findings. During operation of the rolling stock elements with different strength level origin of damages on metallic surfaces is caused by a simultaneous load action. Taking into account that forming of breakdown sites is largely determined by the state of metal volumes nearby the places of maximal active voltages, the technology development of defect accumulation slowdown or the level of active voltages development allow one to prolong the operating term of rolling stock elements. After electric pulse treatment of the wheel rim fragment the regular changes of metal internal structure corresponded to the hardness changes. The hardness of low carbon steel increases proportional to the increase of the level of cold work by rolling. Alternating bending of the cold-deformed flat is accompanied by strength decrease, which is caused by the metal substructure changes. Originality. The softening process of the cold-worked steel is accompanied by substructure changes, which to a greater extent correspond to the hardening development from the plastic cold-work: dispersion of the dislocation cellular structure, formation of the new sub boundaries and displacement of the formed sub boundaries. Practical value. Introduction of electric pulse treatment in the conditions of railway depots repair base allow one to attain the required level of softening of the cold-worked steel on the wheel thread of railway wheel without heating of metal. The given treatment reduces the metal hardness and prolongs the term of incisors use during the renovation of the rolling profile of the railway wheel
|
<li> <b>railway car:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"railway car\" is not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 148,
"label": "vehicleType",
"start": 137
}
] | null | null |
25fd8aae-0208-421e-ac23-f11d628e9a22
|
pending
| 2025-04-09T16:14:38.079327 | 2025-05-13T09:37:44.638574 |
ca63fd7b-d2a0-40dc-8f45-0e2a217e6bbf
|
В роботі представлено узагальнені результати, що стосуються розвитку процесів пом'якшення в холоднодеформованій вуглецевій сталі під час подальшого її знакозмінного навантаження. Процес пом'якшення наклепаної сталі супроводжується субструктурними змінами, які більшою мірою притаманні зміцненню від холодної пластичної деформації: диспергування дислокаційної чарункової структури, формування нових та переміщення сформованих субмеж. Впровадження в умовах ремонтної бази залізничних депо технології електричної імпульсної обробки дозволить без застосування нагріву металу досягти необхідного рівня пом'якшення сталі, наклепаної по поверхні кочення
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[
"Do not acknowledge the language. --> DISCARDED"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"discarded"
] |
[] | null | null |
693509bc-b375-4667-86c2-a60dc013326a
|
completed
| 2025-04-09T16:14:38.079333 | 2025-04-11T13:44:44.291036 |
3d5df478-fc70-4135-941f-8d32bda1e990
|
Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.
|
<li> <b>Autonomous vehicles (AVs):</b> Other Vehicle<li> <b>AVs:</b> Other Vehicle<li> <b>bus driver:</b> Bus<li> <b>camera sensors:</b> Camera<li> <b>bus:</b> Bus
|
[
[
{
"end": 25,
"label": "vehicleType",
"start": 0
},
{
"end": 130,
"label": "vehicleType",
"start": 127
},
{
"end": 966,
"label": "sensorType",
"start": 952
},
{
"end": 658,
"label": "vehicleType",
"start": 655
},
{
"end": 1003,
"label": "vehicleType",
"start": 1000
},
{
"end": 595,
"label": "vehicleType",
"start": 576
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"shuttle\" is another common wording for the autonomous buses/minibuses used in CCAM. So \"shuttle\" should be added in the vehicle type\n- Contextually \"use cases\" in the last sentence could be regarded as a Scenario, so the scenarioType in this case could be \"autonomous shuttle\" and could be added to the \"F31\" cell of tab \"Definition v1.1\" of our Data Model\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 25,
"label": "vehicleType",
"start": 0
},
{
"end": 24,
"label": "vehicleType",
"start": 21
},
{
"end": 130,
"label": "vehicleType",
"start": 127
},
{
"end": 665,
"label": "vehicleType",
"start": 655
},
{
"end": 966,
"label": "sensorType",
"start": 952
},
{
"end": 658,
"label": "vehicleType",
"start": 655
},
{
"end": 1003,
"label": "vehicleType",
"start": 1000
}
] | null | null |
1f249f9c-1431-44e2-bca7-adae83a08109
|
completed
| 2025-04-09T16:14:38.079339 | 2025-05-26T13:44:05.838714 |
1c2a8ce2-e9f8-4b93-b6e0-a78b36cc423e
|
Using a threshold on the reconstruction error, we are able to determine whether a video frame is normal or abnormal (Figure 12).A predefined threshold is not a robust method since our solution should operate in real-time and support multiple camera sensors as we mentioned earlier in the design principles.A fixed threshold value can alter the sensitivity of the event detection, rendering it inappropriate in some scenarios.In addition, a wrong threshold can prevent the detection of certain abnormal events or produce false positives.In order to solve this issue, we introduce a variable thresholding technique in order to find the optimal value in real-time.The initialization procedure now includes a "warm-up" session, in which we aggregate the individual regularity score of each frame.During that session, no detections are performed, as we consider the events as regular.As the buffer continues to fill, we are able to calculate the average reconstruction error and provide a threshold value tailored to the specific conditions.Figure 13 indicates an abnormal scenario with the aforementioned metrics.
|
<li> <b>camera sensors:</b> Camera
|
[
[
{
"end": 256,
"label": "sensorType",
"start": 242
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 256,
"label": "sensorType",
"start": 242
}
] | null | null |
4270bbfc-5463-4f08-ad69-fd34106e763a
|
completed
| 2025-04-09T16:14:38.079346 | 2025-05-26T14:11:44.140478 |
2744013e-a0bb-4be4-b7ac-99d726011b35
|
This paper presents a comparative analysis of the effects of short-range and long-range electric vehicles charging on transformer life. Long-range vehicles are expected to become more common in the future. They have higher battery capacity and charge at higher power levels, modifying demand profile. A probabilistic analysis is performed using the Monte Carlo Simulation, evaluating the transformer hottest-spot temperature and the aging acceleration factor. Residential demand is modeled based on real electricity measurements, and EVs’ demand is modeled based on real data collected from a trial project developed in the United Kingdom. Simulations are conducted considering the influence of ambient temperature analyzing summer and winter seasons and several EV penetration levels. Results show the impacts caused by long-range vehicles are more severe because they charge at higher power levels, especially during winter, when residential demand is higher. For penetration level of 50% during summer, the use of long-range EVs brings a minimum equivalent aging factor of 5.2, which means the transformer aged 124.8 h in a cycle of only 24 h, decreasing its lifetime.
|
<li> <b>electric vehicles:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>EVs:</b> Car
|
[
[
{
"end": 105,
"label": "vehicleType",
"start": 88
},
{
"end": 105,
"label": "vehicleType",
"start": 97
},
{
"end": 155,
"label": "vehicleType",
"start": 147
},
{
"end": 840,
"label": "vehicleType",
"start": 832
},
{
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"label": "vehicleType",
"start": 534
},
{
"end": 1031,
"label": "vehicleType",
"start": 1028
},
{
"end": 765,
"label": "vehicleType",
"start": 763
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"EVs\" could be other vehicles than cars\n- \"EV\" is not recognized as \"vehicleType\" in its singular format\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 105,
"label": "vehicleType",
"start": 88
},
{
"end": 105,
"label": "vehicleType",
"start": 97
},
{
"end": 155,
"label": "vehicleType",
"start": 147
},
{
"end": 840,
"label": "vehicleType",
"start": 832
},
{
"end": 537,
"label": "vehicleType",
"start": 534
},
{
"end": 1031,
"label": "vehicleType",
"start": 1028
}
] | null | null |
b67eec04-66d6-4a47-9d53-9760970f5c5d
|
completed
| 2025-04-09T16:14:38.079352 | 2025-05-26T08:42:45.905977 |
83145c55-6a3a-492b-8ba7-cbf6a7b40f5d
|
This paper analyzed the impacts of long-range EVs on transformer life and compared results with the effects caused by short-range EVs.A probabilistic analysis is employed using the Monte Carlo Simulation.Residential demand is modeled based on real household curves from the UK Data Service, and EVs' pattern is modeled based on data collected from the Electric Nation Project developed United Kingdom.Several EVs' penetration levels are analyzed from 0% to 60% in steps of 10% for winter and summer seasons.From the results, important conclusions can be addressed:
|
<li> <b>EVs:</b> Car
|
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"submitted"
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[
"\"EV\" is not a car exclusively (could be 2-wheeler, 3-wheeler, minivan, etc.)"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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] | null | null |
850c1eea-542f-4f13-b39e-b927ccd1657a
|
completed
| 2025-04-09T16:14:38.079358 | 2025-05-26T09:43:45.144007 |
8b309dad-7739-42be-83f8-c5bfbe23ab28
|
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential driving risks. Therefore, this study proposes a deep-reinforcement-learning-based driving decision algorithm to address these problems. In this study, a non-deterministic vehicle driving risk assessment method is proposed for intersection scenarios and introduced into a learning-based intelligent driving decision algorithm. In addition, this study proposes an attention network based on state information. In this study, a typical intersection scenario was constructed using simulation software, and experiments were conducted. The experimental results show that the algorithm proposed in this paper can effectively derive a driving strategy with both driving efficiency and driving safety in the intersection driving scenario. It is also demonstrated that the attentional neural network designed in this study helps intelligent vehicles to perceive the surrounding environment more accurately, improves the performance of intelligent vehicles, as well as accelerates the convergence speed.
|
<li> <b>Intersection scenarios:</b> Other Scenario<li> <b>vehicle:</b> Other Vehicle<li> <b>intersection scenarios:</b> Other Scenario<li> <b>intelligent vehicles:</b> Other Vehicle<li> <b>intersection scenario:</b> Other Scenario
|
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] | null | null |
92ab985f-29aa-489a-8679-a19612a1b5c5
|
completed
| 2025-04-09T16:14:38.079364 | 2025-05-26T07:05:54.724816 |
66a77e99-7edf-4dda-aae2-e3f924d3e8bc
|
For the intersection scenario, this study uses time to enter (TTE) to evaluate the driving condition of intelligent vehicles through the intersection scenario.The TTE indicates the time that the vehicle continues to travel in its current state of motion from its current position until it enters the collision zone.The calculation process is as follows. In the above equation, t TTE j denotes the TTE value of the intelligent vehicle relative to the potential collision region c j ; a AV denotes the acceleration of the intelligent vehicle; d j denotes the distance of the intelligent vehicle from its current position along the road curvature to the safety line corresponding to the potential collision region c; and v AV denotes the travel speed of the intelligent vehicle.However, the existence of an intersection region between the future path of the vehicle in question and the future path of the intelligent driving vehicle does not necessarily mean that a collision will occur between them.Therefore, assessing the driving risk in the current traffic environment requires using the kinematic information of the intelligent vehicle and the related vehicle to determine the possibility of collision in the corresponding potential collision region.In this study, we use the temporal overlap of intelligent and related vehicles through the potential collision region to determine whether they will collide in the corresponding potential collision region.The mathematical expression of this temporal overlap degree is as follows. In the above formula, t s j indicates the time when the vehicle drives into the safety line corresponding to the potential collision area c j ; t e j indicates the time when the vehicle drives out of the end line corresponding to the potential collision area c j ; d s j is the distance when the vehicle drives from the current position along the road curvature to the safety line corresponding to the potential collision area c j ; d e j is the distance when the vehicle drives from the current position along the road curvature to the end line corresponding to the potential collision area c j ; v is the current driving speed of the vehicle; a is the acceleration of the vehicle; and l is the body length of the vehicle. By substituting the information of the map, the intelligent vehicle, and the related vehicles into Equation ( 6), we can obtain the time intervals [t s AV j , t e AV j ] and [t s SV j , t e SV j ] that the intelligent vehicle and the related vehicles pass in the potential collision region c j .By analyzing the overlap of these two-time intervals, we can determine whether a collision occurs between the intelligent vehicle and the vehicle of interest in the potential collision region c j .According to the different temporal arrangements of t s AV j , t e AV j , t s SV j and t e SV j , we can classify them into six cases, as shown in Figure 5. Except for the cases of 5 and 6 , there is an overlap of time intervals between the intelligent vehicle and the related vehicle in passing the potential collision area c j , and the intelligent vehicle needs to make reasonable driving decisions to avoid the collision.In the cases of 5 and 6 , there is no time interval overlap between the intelligent vehicle and the related vehicle in passing the potential collision area c j , and the intelligent vehicle can pass safely. World Electr.Veh.J. 2023, 14, x FOR PEER REVIEW 10 of 28
|
<li> <b>intersection scenario:</b> Other Scenario<li> <b>intelligent vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>intelligent driving vehicle:</b> Other Vehicle
|
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[
"submitted"
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[
"\"vehicles\" was not identified as \"other vehicle\" --> included as vehicleType"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
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] | null | null |
93d34645-3b78-4833-80f9-7d58008ff05c
|
completed
| 2025-04-09T16:14:38.079370 | 2025-05-26T14:27:41.188959 |
272715f9-b2fe-44d0-801d-0c62eb82367d
|
AbstractThe rail vehicle door system is one of the key components of rail vehicles. Its failure rate accounts for more than 30% of vehicle failures. By analyzing early warnings provided by subhealth data from the door system, the efficiency and reliability of their health maintenance can be effectively improved and stable operation of the door system can also be guaranteed. In this paper, early-stage resistance changes in the subhealth state of rail vehicle door systems are considered as the research object. Firstly, the distribution rules for the motor parameters are studied, and the time-domain and normal operating envelope features of the operating motor are extracted. Secondly, subhealth conditions with different resistances are simulated using a test rig, and the experimental data are applied to summarize the rules. According to the subhealth types and the distribution of features, diagnostic rules for subhealth are formulated. To check the possibility of fault diagnosis, a verification using running rail vehicle door system data is carried out in MATLAB. The results reveal that the misdiagnosis rate of resistance subhealth is 0% while the rate of missed diagnoses is 2%. Meanwhile, the diagnostic process based on the established rules is relatively efficient. This method is suitable for application for resistance subhealth diagnosis of urban rail vehicle door systems.
|
<li> <b>rail vehicle:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
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[
"\"rail vehicle\" is not of interest to CCAM"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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}
] | null | null |
fbbc2ec3-59d5-496b-a877-3ca63ac80f37
|
completed
| 2025-04-09T16:14:38.079376 | 2025-05-13T06:47:14.624269 |
24746c9f-e4f3-44c8-97e7-16fd149201e9
|
Real running data from a door system on a certain line in Nanjing (a city of China) were analyzed and used to verify the typical subhealth rules proposed above.Firstly, some off-peak normal data were selected and trained to construct the statistical envelope feature thresholds.Then, 3000 sets of real data were selected to verify the rules.The real-time M 11 and M 21 represent the number of points over the 3r upper limit and below the 3r lower limit, respectively, in the rising speed section.Similarly, N 11 and N 21 represent the number of points over the 6r upper limit and below the 6r lower limit, respectively, in the rising speed section.M 14 represents the number of points over the 3r upper limit in the whole process.For the meanings of the other parameters, see the body text above. (a) Opening door process (b) Closing door process The diagnostic results contain typical subhealth conditions, such as a local resistance increase in the rising speed section of the opening door, a serious resistance increase in the whole process of the opening door, a local resistance increase in the slow speed section of the closing door, and a local resistance increase in the uniform section of the closing door. According to the maintenance history of door systems, the problem of local resistance increasing basically occurs during the peak commuting hours.At that time, passengers squeeze between the doors, but the of the door system is not affected for a short period of time.Meanwhile, a serious increase in the resistance of the whole door is caused by poor lubrication of the long guide column, which returns to normal after relubrication following internal inspection. Part of the typical subhealth data diagnosed is compared with the normal data according to the established rules in Fig. 12.In Fig. 12a, compared with normal data, the opening door current signals diagnosed with a resistance anomaly in the whole process and in the uniform speed section of the opening door increase at corresponding positions.Also, in Fig. 12b, for the resistance anomaly for slow speed and uniform speed sections of the closing door, the current signals are higher than the normal data signals at the corresponding points.Therefore, the results are consistent with actual working conditions.The statistics of the diagnosis results using the subhealth rules are presented in Table 12.Note that there are 2995 door openings and closings in total, for which the subhealth state is completely diagnosed with a misdiagnosis rate of 0%. From the data judged to be normal, 200 randomly sampled sets are used to test the subhealth diagnostic rules.Table 13 presents the missed diagnosis statistics, showing that four groups of subhealth data are not diagnosed, corresponding to a missed diagnosis rate of 2%. Overall, the diagnosis results based on the typical subhealth rules indicate that they are successful.In consideration of the low rate of missed diagnoses, the rules can not only filter out typical faults and abnormal data but also cover most of the whole and local resistance subhealth states.The average diagnostic time for a single datum was only 0.1945 s, thus fully meeting the needs for online realtime diagnosis by the server. In addition, the signals with missed diagnoses mainly correspond to data for local resistance increases in the slow section of the closing door.Due to the locked current, the peak distribution in the termination section of the closed door is unstable, which may result in a small probability of missed diagnosis or misdiagnosis.Further work will continue to optimize the rules and reduce the missed diagnosis rate.For the diagnosed subhealth data, the real-time subhealth levels could be divided according to their severity, In the future, the rules will be continuously improved, and new subhealth types will be added to improve the diagnosis efficiency and accuracy of the subhealth rules and achieve early warning of failures.The reliability and safety of urban rail vehicle door systems will thereby be effectively improved. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.
|
<li> <b>rail vehicle:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"\"... urban rail vehicle door systems ...\" does not seem to refer to vehicle"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
"start": 4000
}
] | null | null |
39e2a35d-dbeb-447a-af2e-a1c8464baee6
|
completed
| 2025-04-09T16:14:38.079382 | 2025-05-26T07:55:21.248968 |
b74c7a90-4e66-4a33-9cce-f07703df4a55
|
In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>commercial vehicle:</b> Truck<li> <b>vehicles:</b> Other Vehicle
|
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[
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[
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[
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[
"Correct"
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[
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[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
{
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},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 1357
}
] | null | null |
f67a5e6a-7728-4f30-8d9f-e44c746b833a
|
completed
| 2025-04-09T16:14:38.079388 | 2025-05-19T11:39:58.352336 |
ea26d155-dcf3-465b-a7ba-a5ee9452f766
|
Like the existing references [23][24][25][26], the single-pedal regenerative braking strategy proposed in this paper is verified in the simulation, and the braking energy recovery rate and effective braking energy recovery rate are increased by 20.3% and 3.5%, respectively.However, the simulation cannot fully simulate the actual road environment factors.The real-vehicle verification can take into account the system integration, provide more accurate and real data, and evaluate the performance, safety and reliability of the strategy.Therefore, this paper carried out a constant-speed car-following experiment.The results show that the maximum optimization rate of energy consumption of the single-pedal regenerative braking control strategy proposed in this paper reaches 5.81%, and the average optimization rate reaches 4.33%, which can effectively reduce energy consumption and improve the economy of single-pedal pure electric commercial vehicles. The fuzzy control method used in this paper is based on experience and has high requirements for researchers.Therefore, future research can explore the combination of neural networks and swarm intelligence algorithms to further improve the performance of the single-pedal regenerative braking control strategy.By combining the learning ability of neural networks with the optimization ability of swarm intelligence algorithms, a more intelligent and adaptive regenerative braking control strategy can be realized.This will bring greater improvements to the energy recovery and driving experience of the vehicle's braking system.
|
<li> <b>commercial vehicles:</b> Truck<li> <b>vehicle:</b> Other Vehicle
|
[
[
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},
{
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},
{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"In \"...car-following...\" the term \"car\" was not captured"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 954,
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},
{
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"label": "vehicleType",
"start": 365
},
{
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"label": "vehicleType",
"start": 1559
}
] | null | null |
f20cef8c-c04c-4a7e-8cb9-100192bc1066
|
completed
| 2025-04-09T16:14:38.079394 | 2025-05-13T16:03:04.154399 |
f0e128dc-b57d-46be-9543-1d8c5893fe50
|
This study analyses the technical and economic aspects of a novel subsea freight glider (SFG). The SFG is an excellent replacement for tanker ships and submarine pipelines transporting liquefied CO2. The main aim of the SFG is to ship CO2 from an offshore facility to an underwater well where the gas can be injected; as an advantage, the SFG vehicle may be used to transport all kinds of cargo. The SFG travels below the sea surface, making the vessel weather-independent. The research is divided into two steps. Firstly, the technical feasibility analysis is performed by designing a baseline design with a length of 56.5 m, a beam of 5.5 m, and a cargo volume of 1194 m3. The SFG is developed using DNVGL-RU-NAVAL-Pt4Ch1, which was initially created for military submarine designs. Two additional half-scaled 469 m3 and double-scaled 2430 m3 models are created when the baseline design fulfils the technical requirements. Secondly, the economic analysis is carried out using the freely accessible MUNIN D9.3 and ZEP reports. The economic feasibility analysis is illustrated through a case study with a CO2 transport capacity range of 0.5 to 2.5 mtpa (million tons per annum) and a transport length range of 180 km to 1500 km. The prices of CO2 per ton for the SFG, crew and autonomous tankers, and offshore pipelines are comprehensively compared. According to the results, SFGs with capacities of 469 m3, 1194 m3, and 2430 m3 are technically possible to manufacture. Moreover, the SFGs are competitive with a smaller CO2 capacity of 0.5 mtpa at distances of 180 and 500 km and a capacity of 1 mtpa at a distance of 180 km.
|
<li> <b>subsea freight glider (SFG):</b> Other Vehicle<li> <b>SFG:</b> Other Vehicle<li> <b>tanker ships:</b> Other Vehicle<li> <b>submarine:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>vessel:</b> Other Vehicle<li> <b>tankers:</b> Other Vehicle
|
[
[
{
"end": 350,
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"start": 343
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Marine-related vehicles are not of interest to CCAM pilot, thus classification and annotation is conbducted accordingly"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
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{
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{
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{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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"label": "vehicleType",
"start": 343
},
{
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"label": "vehicleType",
"start": 446
},
{
"end": 1295,
"label": "vehicleType",
"start": 1288
}
] | null | null |
bfb74706-02c2-42a1-b176-54626f324668
|
completed
| 2025-04-09T16:14:38.079400 | 2025-05-26T06:54:09.904520 |
86ca55a3-cd1c-40f3-b02a-845d1403939c
|
The minimum number of vessels required to perform the mission is illustrated in Figure 7.
|
<li> <b>vessels:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"A vessel is not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 29,
"label": "vehicleType",
"start": 22
}
] | null | null |
09c13aa1-b0cd-445a-a66e-c4815f1cd3fd
|
completed
| 2025-04-09T16:14:38.079406 | 2025-05-20T14:42:25.618810 |
8a019c2e-cb41-4c43-987f-5150703d6468
|
Existing research concerning the extention of the service life of various types of railway vehicles and assessing its remaining lifetime has been reviewed and analyzed. It has been established that the vast majority of studies relate to the assessment of the residual lifetime of various types of railway rolling stock based on the results of technical diagnostics and routine tests, as well as the assessment of corrosion wear of supporting elements and vehicles bodies. At the same time, little attention has been paid to the issue of improving existing programs and procedures of complex technical diagnostics. It was determined that the set of diagnostics operations for the extension of the service life includes routine tests of a test sample and examination of the technical condition of the metal structure of each railway vehicle for mechanical and corrosive damage.It is proposed to make changes to the existing current programs and procedures in such key sections as: terminology, objects of technical diagnostics and tests, selection of a test sample for routine tests, execution order and methods of technical diagnostics and routine tests, data processing and evaluation of results. A comprehensive approach for assessing the residual lifetime of railway vehicles is also proposed.
|
<li> <b>railway vehicles:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
[
[
{
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"label": "vehicleType",
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},
{
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},
{
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},
{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"railway vehicle\" not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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{
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{
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{
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{
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},
{
"end": 838,
"label": "vehicleType",
"start": 831
}
] | null | null |
a5884aad-fe75-4c54-82f4-18c52547ca08
|
completed
| 2025-04-09T16:14:38.079412 | 2025-05-20T14:36:36.559025 |
f1c12cdd-071d-4ce9-8b85-fdb73b1e0e72
|
The purpose of the impact endurance tests is to experimentally check the durability of the metal structure of a sample of a specific type vehicle body with actual (after the intended period of operation) thicknesses of elements under specified shock load modes, which are equivalent in terms of destructive effect to the loading of the car by operational longitudinal dynamic forces.When carrying out impact endurance tests the following indicators are determined: impact force on the coupling; number of impacts; the presence of permanent deformations, damage from impact loads in the inspected elements of the body.At the same time, it is allowed to assess the durability of the load-bearing elements of the body structure of the passenger car and the locomotive by the fatigue resistance factor, which is calculated based on the results of dynamic and strength tests in accordance with the requirements of the "Standard for the calculation and design of new and modernized railway cars of MPS gauge 1520 mm (non-self-propelled) » [30] and RD 24.050.37 [31].During dynamic strength tests, dynamic stresses in the main load-bearing elements of the body structure which occur during the movement of wagons at different speeds, up to the design one, on specific sections of the railway track of the corresponding design and current condition are determined and evaluated.During dynamic strength tests, in accordance with the requirements [31], such indicators as the natural frequency of the main vertical bending oscillations of the body and the fatigue resistance factor of the main bearing elements of the body are determined. Impact endurance tests are carried out in the following sequence: -a vehicle is equipped with a special auto-coupling dynamometer, static load pre-graded up to 400 tons; -loading to the nominal carrying capacity (for freight cars); loading to the maximum passenger capacity and provision with water and coal (for passenger cars); complete servicing (for locomotives); -weighing of loaded/equipped vehicles; -location of railway vehicles on the test site with a shunting hump test stand with a buffer stop, equipped with a self-coupling device, and preparation of a striker, the weight of which must be no less than the weight of the test car.At the same time, it is allowed to use a railway locomotive instead of a shunting hump test stand, and instead of a railway stop -a formation of several wagons, the total weight of which is at least 300 tons; -direct impact test; -measurement of stresses, rolling speed of the tank car with the aid of measuring equipment; -inspection of the metal structure of the rolling stock is performed after every 3-5 impacts. The process of carrying out shock endurance tests using a locomotive and an experimental coupling as a railway stop is shown in Fig. 3.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>car:</b> Car<li> <b>passenger car:</b> Car<li> <b>locomotive:</b> Other Vehicle<li> <b>wagons:</b> Other Vehicle<li> <b>railway cars:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>tank car:</b> Other Vehicle<li> <b>rolling stock:</b> Other Vehicle
|
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] |
[
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] |
[
"submitted"
] |
[
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] |
[
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] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"locomotive\", \"railway car\", \"wagon\", \"tank car\", \"rolling stock\" not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
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{
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},
{
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{
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},
{
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},
{
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},
{
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"label": "vehicleType",
"start": 2636
}
] | null | null |
2cdb795e-6a4e-4acc-8eda-c68800914313
|
completed
| 2025-04-09T16:14:38.079418 | 2025-05-13T06:37:33.729689 |
37c0fa96-4638-48b6-b259-079d1219ed3d
|
Future 5G systems have set a goal to support mission-critical Vehicle-to-Everything (V2X) communications and they contribute to an important step towards connected and automated driving. To achieve this goal, the communication technologies should be designed based on a solid understanding of the new V2X applications and the related requirements and challenges. In this regard, we provide a description of the main V2X application categories and their representative use cases selected based on an analysis of the future needs of cooperative and automated driving. We also present a methodology on how to derive the network related requirements from the automotive specific requirements. The methodology can be used to analyze the key requirements of both existing and future V2X use cases.
|
<li> <b>Vehicle-to-Everything (V2X):</b> V2X<li> <b>connected and automated driving:</b> Other Level of Automation<li> <b>automated driving:</b> Other Level of Automation<li> <b>V2X:</b> V2X
|
[
[
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},
{
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]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Included a few more communicationType instances:\n-5G\n-V2X communication\n-communication technologies\n-V2X application\n-V2X use cases\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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[
{
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{
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{
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{
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},
{
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{
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"label": "entityConnectionType",
"start": 416
},
{
"end": 780,
"label": "entityConnectionType",
"start": 777
}
] | null | null |
f53553f8-73df-4d42-a753-d9e6f5865299
|
completed
| 2025-04-09T16:14:38.079424 | 2025-05-19T09:50:29.166189 |
c5591c3f-7895-497f-8225-8475cf2deba9
|
Improved orchestration capabilities able to cope with the unique requirements of vehicular use cases and with improved network management and re-configurability capabilities to cope with the dynamicity in terms of traffic demand in vehicular scenarios. Finally, it is worth mentioning that 3GPP is working on enhancing the C-V2X technology within the further 3GPP New Radio (NR) framework [35].IEEE is, at the same time, working on enhancing 802.11p in the new project P802.11bd, with the aim to increase throughput, range, and improve procedures for positioning [36].The main challenges are related to the design of the new V2V broadcast, groupcast, and unicast SL communication interfaces to support the new demanding KPIs, e.g., the high data rate for cooperative perception, high reliability, and latency for remote driving.
|
<li> <b>vehicular use cases:</b> Other Scenario<li> <b>vehicular scenarios:</b> Other Scenario<li> <b>C-V2X:</b> Cellular (4G/5G)<li> <b>802.11p:</b> ITS-G5<li> <b>V2V:</b> V2V<li> <b>remote driving:</b> Other Scenario
|
[
[
{
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},
{
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},
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{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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},
{
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},
{
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},
{
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"label": "entityConnectionType",
"start": 625
},
{
"end": 827,
"label": "scenarioType",
"start": 813
}
] | null | null |
29988c68-8141-41ee-9cec-fdf09eec826b
|
completed
| 2025-04-09T16:14:38.079430 | 2025-05-13T06:27:28.299600 |
15bac94d-705e-4849-8d91-5bb1d43f729b
|
Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.
|
<li> <b>hybrid electric vehicles:</b> Car<li> <b>vehicles:</b> Other Vehicle
|
[
[
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
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},
{
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]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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},
{
"end": 1010,
"label": "vehicleType",
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},
{
"end": 97,
"label": "vehicleType",
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},
{
"end": 185,
"label": "vehicleType",
"start": 177
},
{
"end": 1010,
"label": "vehicleType",
"start": 1002
}
] | null | null |
a91a3417-d30f-4a37-8339-16ffe6a6b94a
|
completed
| 2025-04-09T16:14:38.079436 | 2025-05-19T09:41:32.338452 |
2edfa996-a045-4952-8311-a885b47d3824
|
Comprehensive Coverage of Latest Advancements: The paper offers a comprehensive coverage of the latest advancements in HESS technology.By synthesizing information scattered across the literature, it presents an up-to-date account of the most recent developments, ensuring a thorough understanding of the current state-of-the-art in this field.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
2e4f3a25-5d5d-443f-b4ec-a4db03dd6b97
|
completed
| 2025-04-09T16:14:38.079442 | 2025-05-19T09:41:17.870634 |
6d851a60-22ec-44a8-a9c7-7a79379c7b3f
|
Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed kernel function (MK-SVR) combined with the polynomial kernel (PK) function and radial basis function (RBF). A mathematical model of the maneuvering of the USV was established and subjected to a zig-zag test on the DW-uBoat USV platform to obtain the test data. Cross-validation was used to optimize the parameters of SVR and determine suitable weight coefficients in the MK function to ensure the adaptive adjustment of the proposed method. The PK-SVR, RBF-SVR, and MK-SVR methods were used to identify the dynamics of the USV and build the corresponding predictive models. A comparison of the results of the predictions with experimental data confirmed the limitations of the SVR with a single kernel function in terms of forecasting different parameters of motion of the USV while verifying the validity of the MK-SVR based on data collected from a full-scale test. The results show that the MK-SVR method combines the advantages of the local and global kernel functions to offer a better predictive performance and generalization ability than SVR based on the nuclear kernel function. The purpose of this manuscript is to propose a novel method of dynamics identification for USV, which can help us establish a more precise USV dynamic model to design and verify an excellent motion controller.
|
<li> <b>unmanned surface vehicle (USV):</b> Other Vehicle<li> <b>USV:</b> Other Vehicle
|
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"USV is not of interest for CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
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{
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"label": "vehicleType",
"start": 1476
}
] | null | null |
64b63366-528b-42d3-b898-7ff9cae6e5e1
|
completed
| 2025-04-09T16:14:38.079448 | 2025-05-19T11:50:27.227644 |
e916bdab-02d8-4272-b63d-cff349d72708
|
The SVM is a statistical method based on supervised learning that was proposed in the 1990s [9].The objective of the SVM is to obtain the equation of the hyperplane of structural risk minimization.In the context of regression, the SVM is called the SVR.For the training data {(x i ,y i ), i = 1, 2, . . .N}, where x i ∈ R n is the input data, y i ∈ R is the target value for x i and N is the number of training samples.The SVR equation of the hyperplane is presented as: where ω denotes the matrix of weights and b denotes the bias constant. In contrast to traditional regression algorithms, SVR enables the maximum deviation ε between f(x) and y i .The value of the loss function l ε is chosen according to the following formula: Therefore, the equation of the hyperplane can be obtained by minimizing the optimization problem as follows: where C is the regularization parameter.Slack variables ξ i and ξi were selected to make the model more robust so that it can handle unfeasible constraints of the optimization problem as follows: The optimal solution can be obtained by solving the dual form of Equation (7).The Lagrange multipliers were imposed to solve this problem: where µ i ≥ 0, μi ≥ 0, α ≥ 0, α ≥ 0. We then computed the derivatives of ω, b, e i , and α i , and set them to zero to obtain the following: By placing Equation ( 9) into the Lagrangian function, the dual problem can be expressed as follows: Equation ( 10) meets the Karush-Kuhn-Tucker conditions as follows: The following solution can be acquired by calculating the dual problem:
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
abcc5e71-0c9a-4e85-af38-99118d519cb6
|
completed
| 2025-04-09T16:14:38.079454 | 2025-05-26T08:02:19.809132 |
0b78a601-c183-4653-a354-d00225c72ca1
|
As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.
|
<li> <b>electric vehicle:</b> Car<li> <b>vehicles:</b> Other Vehicle
|
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[
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[
"submitted"
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[
"Partially correct"
] |
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"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"electric vehicle\" can be 2-wheeler, 3-wheeler or minivan and not a car exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
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] | null | null |
8e3411ab-af30-4ec0-8f75-71b7dd1236ea
|
completed
| 2025-04-09T16:14:38.079460 | 2025-05-13T09:02:01.476670 |
0ef17725-5e23-43bb-b58a-d2c35c55405c
|
In the following, results of the flexibility estimation are shown which consider weights in the aggregation process from individual profiles, e.g., for uncontrolled charging, to the estimated average EV profiles.As shown in Table 3, this flexibility is described by the four profiles' battery drain drain elec (t), maximum charging capacity p charge, max (t) as well as minimum (SoC min (t)) and maximum SoC (SoC max (t)).A fifth profile, uncontrolled charging charge unc (t), is calculated as well, assuming vehicles are charged as soon as a grid connection is available. Figure 3 shows the counteracting effects of increased travel distances in a lower number of trips [18].Charging is available when vehicle owners are at home which is mainly during the morning, evening and night.While daytime grid connection shares are higher based on the MiD17 data set during workdays, the opposite is the case in a milder intensity for the weekend.This can be explained by almost doubling average trip distance travelled on Sunday, compensating for less trips being carried out.However, as described above, this effect is small compared to the overall change in fleet grid connection share.Compared to the differences in fleet grid connection shares, changes in mobility patterns imply stronger changes in electricity outflow from the battery for driving as depicted in Figure 4 since mobility changes directly translate into drain changes.Increased average trip distance travelled between 9:00 and 17:00 leads to increased electricity consumption for driving mainly from 8:00 to 17:00.In its peak at 16:00, the average electricity consumption is 0.3 kWh/h based on MiD08, whereas it is 0.55 kWh/h based on MiD17.The average electricity consumption for driving shows characteristic morning and evening peaks throughout the week.The maximum in fleet consumption for driving drain elec (t) can be observed for evenings with approximately 0.6 kWh/h per vehicle on average.Increased average trip distance in MiD17 compared to MiD08 leads to higher electricity consumption at almost every hour.Sunday differences in consumption are higher by almost a factor of 2, from 0.35 kWh/h to 0.7 kWh/h on average.Saturdays show a broader travel characteristic during daytime the but peaking below Tuesday evening travels. Uncontrolled charging shown in Figure 5 differs between the two samples in the time interval between 16:00 and 21:00 with its peak at 18:00.In this peak, charge unc (t) results in 0.5 and 0.8 kW for MiD08 and MiD17, respectively.The patterns in the uncontrolled charging scenario are thus quite similar. The trends described for an average day are mirrored by weekly EV fleet uncontrolled charging.The peak broadened a bit and increased in size from under 0.6 to 0.8 kW.On Fridays and Saturdays uncontrolled charging is more distributed throughout the day with increasing peaks in the evenings.Sundays show the all-week high slightly below 1 kW on average, this having increased more than two-fold showing the stark travel increase on Sundays between the MiD08 and MiD17. SoC profiles shown in Figure 6 depict a temporally dependent average battery range for load shifting.This battery range is reduced by a reduced maximum SoC and an increased minimum SoC at daytimes when vehicles on the road cannot charge but need a minimum of SoC for the planned trip on that respective day. The average vehicle SoC is smaller for the estimation based on the MiD17 dataset, which is due to increased travel distances in the sample.This reduction is most pronounced on Sundays when the two profiles almost touch, implying no charging flexibility for the average vehicle at all.Still, during nights, an average vehicle provides a large source of flexibility with the full accumulator capacity of 50 kWh available for flexible charging. Simple methods for aggregating individual SoC profiles to fleet level as used here have been shown to overestimate the fleet load shifting potential.Mathematically correct methods exist but have been shown to currently be impractical on time horizons employed in the context of energy systems analysis (T >> 10 time steps) [2].The given profiles can be a first indication for a vehicle fleet flexibility, however greater care in applying them in the context of evidence-based decision making should be taken.
|
<li> <b>EV:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
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[
null
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},
{
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}
] | null | null |
2cc8cd2d-36f9-4f78-b5fb-84bc03844e61
|
completed
| 2025-04-09T16:14:38.079466 | 2025-05-13T05:42:33.485907 |
e696ffae-d305-4d3a-8bde-5d762826f21e
|
With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue.
|
<li> <b>electric vehicles (EVs):</b> Car<li> <b>EVs:</b> Car
|
[
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] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"EV in singular is not assigned. There are 2 instances of \"EV users\" where \"EV\" is not linked to \"Car\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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},
{
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},
{
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}
] | null | null |
2d82c421-c409-467b-a473-f4ce00f3758f
|
completed
| 2025-04-09T16:14:38.079472 | 2025-05-23T21:16:57.773097 |
d43cd14c-9f8d-4ee2-aa2f-5c10e6631a55
|
In order to compare the effects of different robust adjustment factors (β) on the VPP's operating revenue, Table 6 below shows the variation in the VPP's operating revenue with different robust adjustment coefficients.In Table 6, as β increases, the operator's use of wind power output becomes gradually more conservative, which then leads to a gradual decrease in the VPP's operating revenue.When β changes from 0 to 1, it indicates that the VPP's attitude toward wind power usage gradually changes from proactive to pessimistic and conservative.However, the charging cost of EV users does not change during this process.This indicates that when the VPP's use of wind turbine output changes, the operator will always prioritize the interests of EV users and will not transfer the risk cost to the charging cost of EV users, but will rely on the energy storage within the VPP, the regulated capacity of the demand response load, or the purchase and sale of electricity in the market to maintain the stable operation of the system.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
ed769c2f-d8b1-4732-b63e-b18e1dca8c46
|
completed
| 2025-04-09T16:14:38.079478 | 2025-05-26T07:56:22.780540 |
bcdc0d6a-6c83-4d8f-b92b-4c2b4e61b4b4
|
In this paper, it is proposed to carry out the assessment of road transport hazards using the minimization of the negative impact by three main components: impact of changes in emissions of toxic elements of pollutants from the exhaust gases of car engines, change of noise pollution of the environment and change of the number of accidents. The method of determination of equivalent losses which will be as a result of the release of a conventional ton of toxic components of pollutants from the exhaust gases of car engines and the impact of noise pollution of the environment on three groups of components – drivers and passengers, pedestrians and residents of adjacent territories is justified. Special attention is paid to determining equivalent losses due to traffic accidents with injured or dead. For example, a study to determine the change in environmental hazards of road transport for the section of Lubinska Str. in Lviv, depending on the main indicator of traffic flow - the speed of traffic was carried out. It is established that for the speed of 25 km/h the minimum environmental damage will be 1093 thousand UAH per year, and the minimum total hazards of road transport, taking into account the possibility of an accident at 12 km/h will be 1239 thousand UAH per year. With the help of the obtained model, it is possible to determine the amount of hazards from road transport, which allows taking into account environmental, social and economic components when studying the levels of the negative impact of transport on the environment and ensuring minimum accident rates on the studied section of the road network. Conducting preliminary theoretical research to find rational solutions when applying schemes to improve traffic organization will be especially useful.
|
<li> <b>car engines:</b> Car<li> <b>pedestrians:</b> Pedestrian
|
[
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] |
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[
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] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
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] |
[
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] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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"label": "vehicleType",
"start": 514
},
{
"end": 646,
"label": "VRUType",
"start": 635
}
] | null | null |
9567b626-6c41-47e7-a4f4-c90e78eb1562
|
completed
| 2025-04-09T16:14:38.079484 | 2025-05-26T13:34:37.668925 |
ab5da189-2ad6-48ea-acbf-4a617da378ac
|
The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles.
|
<li> <b>pedestrian:</b> Pedestrian<li> <b>vehicle:</b> Other Vehicle<li> <b>autonomous vehicles:</b> Other Vehicle<li> <b>LiDAR sensor:</b> LiDAR<li> <b>pedestrian detection:</b> Other Scenario
|
[
[
{
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},
{
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},
{
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"label": "VRUType",
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},
{
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},
{
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},
{
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},
{
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"label": "sensorType",
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},
{
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"start": 736
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"start": 31
},
{
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"label": "VRUType",
"start": 431
},
{
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},
{
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"label": "VRUType",
"start": 736
},
{
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"start": 123
},
{
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"label": "vehicleType",
"start": 511
},
{
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"label": "vehicleType",
"start": 1203
},
{
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"label": "sensorType",
"start": 675
},
{
"end": 756,
"label": "scenarioType",
"start": 736
}
] | null | null |
31df81f7-ee64-4901-996d-215328a774ef
|
completed
| 2025-04-09T16:14:38.079489 | 2025-05-13T06:55:25.520051 |
7a3aa19c-beaa-4321-ab09-51f133b7cd50
|
To compare the pedestrian detection performance between the two agents, we extracted LiDAR bin files for onboard and intersection data from the ROS bag files and label txt files containing pedestrian information.The ROS-ApproximateTimeSynchronizer package was utilized for extraction, and the label files stored label data in the order of x, y, z, dx, dy, dz, heading, and class type for 3D LiDAR detection training.As a result, the number of extracted data for each scenario is shown in Table 7 below.
|
<li> <b>pedestrian:</b> Pedestrian<li> <b>LiDAR:</b> LiDAR
|
[
[
{
"end": 25,
"label": "VRUType",
"start": 15
},
{
"end": 199,
"label": "VRUType",
"start": 189
},
{
"end": 90,
"label": "sensorType",
"start": 85
},
{
"end": 396,
"label": "sensorType",
"start": 391
},
{
"end": 475,
"label": "scenarioType",
"start": 467
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Added scenarioType instance for the term \"scenario\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 25,
"label": "VRUType",
"start": 15
},
{
"end": 199,
"label": "VRUType",
"start": 189
},
{
"end": 90,
"label": "sensorType",
"start": 85
},
{
"end": 396,
"label": "sensorType",
"start": 391
}
] | null | null |
0fab229b-0600-457e-9450-b828fff97d23
|
completed
| 2025-04-09T16:14:38.079496 | 2025-05-19T11:37:37.653877 |
956a3d70-2cdb-4524-b4f5-6cedb2908686
|
The analysis of single vehicle type dynamic marginal cost is extended to multiple vehicle type dynamic one based on time‐dependent multiple vehicle type queue analysis at a bottleneck. First, a dynamic link model to rep‐ resent the interactions between cars and trucks is provided. Then, the analytic expression of a multiple vehicle type dynamic marginal cost function considering departure time choices is deduced under congested and un‐congested conditions and consequently, a dynamic toll function is given. A heuristic algorithm is introduced to solve multiple vehicle type dynamic queues and toll under system optimum and user equilibrium conditions taking into account traveler's departure time. A numerical example shows that a dynamic congestion toll can diminish queues and improve system conditions when traffic demand is not changed.
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>cars:</b> Car<li> <b>trucks:</b> Truck
|
[
[
{
"end": 30,
"label": "vehicleType",
"start": 23
},
{
"end": 89,
"label": "vehicleType",
"start": 82
},
{
"end": 147,
"label": "vehicleType",
"start": 140
},
{
"end": 333,
"label": "vehicleType",
"start": 326
},
{
"end": 573,
"label": "vehicleType",
"start": 566
},
{
"end": 257,
"label": "vehicleType",
"start": 253
},
{
"end": 268,
"label": "vehicleType",
"start": 262
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 30,
"label": "vehicleType",
"start": 23
},
{
"end": 89,
"label": "vehicleType",
"start": 82
},
{
"end": 147,
"label": "vehicleType",
"start": 140
},
{
"end": 333,
"label": "vehicleType",
"start": 326
},
{
"end": 573,
"label": "vehicleType",
"start": 566
},
{
"end": 257,
"label": "vehicleType",
"start": 253
},
{
"end": 268,
"label": "vehicleType",
"start": 262
}
] | null | null |
96b900a3-249e-4b56-b954-4a09a095a1ce
|
completed
| 2025-04-09T16:14:38.079502 | 2025-05-26T08:22:06.876450 |
66db00b5-39ae-452d-8f90-d076f40a4045
|
According to the above analysis, the toll of a car / truck can be written as: where: 1 i s and 2 i s are the times at which the first and last car and truck traveler respectively depart home.Furthermore, 0 a t and 1 a t are the times at which transition from Case 3 to 4 occurs at the early arrival stage and the time at which transition from Case 4 to 3 occurs at the late arrival stage respectively.0 t and 1 t describe respectively the starting and ending time of the total congested queue.The above toll function cannot guarantee a nonnegative toll value, and thus non-negative transformation is given as follows:
|
<li> <b>car:</b> Car<li> <b>truck:</b> Truck
|
[
[
{
"end": 50,
"label": "vehicleType",
"start": 47
},
{
"end": 146,
"label": "vehicleType",
"start": 143
},
{
"end": 58,
"label": "vehicleType",
"start": 53
},
{
"end": 156,
"label": "vehicleType",
"start": 151
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
"start": 47
},
{
"end": 146,
"label": "vehicleType",
"start": 143
},
{
"end": 58,
"label": "vehicleType",
"start": 53
},
{
"end": 156,
"label": "vehicleType",
"start": 151
}
] | null | null |
f806f29a-8ef8-4a2e-8336-df5edb45c89b
|
completed
| 2025-04-09T16:14:38.079509 | 2025-05-26T13:43:50.970748 |
b581f08c-7261-461c-acbe-ee36c3032efc
|
With the rapid development of electric vehicles (EVs), one of the urgent issues is how to deploy limited charging facilities to provide services for as many EVs as possible. This paper proposes a bilevel model to depict the interaction between traffic flow distribution and the location of charging stations (CSs) in the EVs and gasoline vehicles (GVs) hybrid network. The upper level model is a maximum flow-covering model where the CSs are deployed on links with higher demands. The lower level model is a stochastic user equilibrium model under elastic demands (SUE-ED) that considers both demands uncertainty and perceived path constraints, which have a significant influence on the distribution of link flow. Besides the path travel cost, the utility of charging facilities, charging speed, and waiting time at CSs due to space capacity restraint are also considered for the EVs when making a path assignment in the lower level model. A mixed-integer nonlinear program is constructed, and the equivalence of SUE-ED is proven, where a heuristic algorithm is used to solve the model. Finally, the network trial and sensitivity analysis are carried out to illustrate the feasibility and effectiveness of the proposed model.
|
<li> <b>electric vehicles (EVs):</b> Car<li> <b>EVs:</b> Car<li> <b>gasoline vehicles (GVs):</b> Car
|
[
[
{
"end": 53,
"label": "vehicleType",
"start": 30
},
{
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"label": "vehicleType",
"start": 157
},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 880
},
{
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"label": "vehicleType",
"start": 329
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" and \"GVs\" may include other vehicles besides cars as well (2-wheelers, 3-wheelers, minivans, etc.)"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 53,
"label": "vehicleType",
"start": 30
},
{
"end": 52,
"label": "vehicleType",
"start": 49
},
{
"end": 160,
"label": "vehicleType",
"start": 157
},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 880
},
{
"end": 352,
"label": "vehicleType",
"start": 329
}
] | null | null |
6d1395a3-2f09-4e0b-acce-92c971065518
|
completed
| 2025-04-09T16:14:38.079515 | 2025-05-26T08:48:08.149608 |
21a23752-e11e-42b9-bf74-50900c2d4a25
|
This paper studies the deployment of public charging stations in a hybrid network to maximize the service efficiency of the charging facilities.A bilevel model was proposed to depict the interaction between the mixed link flow and the location of the charging stations.The link flow obtained by the lower SUE-ED model is the key to determine the location of the CSs.In the upper level model, the CSs were arranged on the link flow ranking p to achieve the maximum coverage.Four important factors were taken into accounts in the lower level SUE model, including the range limit of EVs that affected the path choice, the elastic travel demand closely related to the distribution of link flow, the road congestion effect, and the capacity of charging facilities in travel costs.These four elements interact with each other and continuously iterate to reach equilibrium, which was more consistent with the actual travel situation.A hybrid integer nonlinear programming method based on the method of successive average (MSA) was constructed to prove the equivalence and the uniqueness of the SUE-ED model with range constraints.Finally, a network trial was conducted to examine the impact of elastic demand between OD pairs, the range limit, charging speed, the charging facilities' utility, and waiting cost on the location problem. It should be noted that the actual road network systems are very diverse, especially in urban areas, and significantly differ from the example of the Nguyen-Dupuis Network.More realistic factors are required to be considered in the future when designing the location of charging stations, not only by technical and operational factors but also by social factors.And assumptions can be relaxed appropriately in future work.The nonlinearity of the charging time, the uncertainties in EVs energy consumption, as well as the bounded rationality of EV travelers, should be considered.
|
<li> <b>EVs:</b> Car
|
[
[
{
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},
{
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"label": "vehicleType",
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},
{
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"start": 1873
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"EV\" was not identified as vehicleType\n- \"EVs\" are not cars exclusively (2-wheelers, 3-wheelers, minivans, etc.)\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 583,
"label": "vehicleType",
"start": 580
},
{
"end": 1814,
"label": "vehicleType",
"start": 1811
}
] | null | null |
cdd6737e-76a6-42d6-bd8e-76754134f948
|
completed
| 2025-04-09T16:14:38.079521 | 2025-05-26T13:52:51.507412 |
ec77dbdf-f93b-4c1d-814c-c953cff3d797
|
The transportation sector has the highest energy demand worldwide and bears the primary responsibility for CO2 emissions. Electromobility has emerged as the most feasible way to alleviate this problem. However, its potential depends heavily on the development of renewable energies. Island regions raise additional barriers to electromobility due to their heavy dependence on fossil fuels. This article addresses this challenge by presenting a comprehensive well-to-wheel framework to assess the levels of efficiency and CO2 emissions of electromobility options such as battery and plug-in electric vehicles (BEVs and PHEVs). The results were compared with those for internal combustion engine vehicles (ICEVs). The framework proposed takes account of various factors including the extraction, refining, and transport of oil, different segments of land vehicles, and electricity system operations. The framework is demonstrated with a case study of the Spanish Canary archipelago. The results show that BEVs improve efficiency and CO2 emissions by around 30% compared to ICEVs on islands where the share of renewable energies is higher than 21%. In contrast, limited renewable generation may lead to BEVs polluting up to 15% more than ICEVs. PHEVs should be considered as a suitable alternative if the share of renewable generation is higher than 35%.
|
<li> <b>battery and plug-in electric vehicles (BEVs and PHEVs):</b> Car<li> <b>BEVs:</b> Car<li> <b>PHEVs:</b> Car<li> <b>internal combustion engine vehicles (ICEVs):</b> Car<li> <b>land vehicles:</b> Other Vehicle<li> <b>ICEVs:</b> Car
|
[
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"BEVs\", \"PHEVs\" and \"ICEVs\" might not be cars exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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{
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{
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{
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{
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{
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},
{
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"label": "vehicleType",
"start": 1071
},
{
"end": 1240,
"label": "vehicleType",
"start": 1235
}
] | null | null |
a7c77ee0-1b3a-4fe8-b8d7-a2cce05c652a
|
completed
| 2025-04-09T16:14:38.079527 | 2025-05-13T06:02:55.774753 |
b10bd68c-190f-468d-a30c-4cb235bbea5b
|
Figure 3 details the energy pathways for each vehicle and the processes covered from the well of the energy source to the vehicle's wheels (given that 96% of the primary energy sources in the Canaries are oil based).In Figure 3, each step (s) of the process and each emission focus (f) are shown along the pathways.The first step is the extraction of crude, which in the Canaries is a mix from more than 20 producing countries.The oil exporting nations most represented in Spain's oil imports are Nigeria (20%), Mexico (15%), Saudi Arabia (10%), Kazakhstan (8%), Iraq (6%), the United States (6%), and Brazil (5%) [28].The average energy consumption for the extraction, flaring, and venting associated with oil extraction (Figure 3, s = 1) for an average yield is between 92.7% and 94.3%.These processes are responsible for emissions allocated out of the Canaries of around 3.75-4.75g CO 2 /MJ (f = 1) [29]. Crude is transported by sea using oil tankers (Figure 3, s = 2), with load capacities from 60,000 to 160,000 tons per trip.The energy consumption of one of these tankers is nearly 49 to 51 tons of fuel per day [16].Table 1 details the distance of the routes to continental Spain and the energy efficiency of transport measured in relative energy losses related to the energy contained in the cargo.The oil refining efficiencies and CO 2 -eq emissions per fuel product from Spanish facilities are not easily accessible due to confidentiality concerns and a lack of industry transparency.The refineries' energy efficiency and emissions depend on the following: (i) the level of technological development; (ii) the type of refinery composition; (iii) the investment in energy efficiency; (iv) the type of crude; (v) the output products produced; (vi) the environmental requirements arising from national legislation; (vii) the location of the plant, which determines the climatic and seasonal factors and the planning and optimization of each process; and (viii) the implementation of the energy management strategies. The crude oil destined to fuel the Canary Island's energy consumption is sourced from various countries and is usually refined in one or more of the eight Spanish refineries.As per Han et al. [29], the efficiencies and emissions from refining processing in the European plants were included in the study (see Table 2).From EuroStat [30], the average consumption of oil derivatives for the last decade (2011-2020) was recorded with the following share range for each product: (i) 3.7-4.1% of LPG; (ii) 44.2-47.0%gasoline; (iii) 26.1-29.5% diesel; and (iv) 2.5-4.2%fuel oil.There was a variation in the energy efficiency of the process (s = 3.1 to 3.4) and the associated emissions for each petroleum product (f = 3.1 to 3.4).In a subsequent step, the final oil-derivate products are separated and transported to the Canary Islands in specific tankers.Therefore, to calculate the efficiency of the process, the calculation of the process's efficiency was simplified by using the diesel energy consumption data of a representative vessel [16].The efficiency of the tanker that transports the oil-derivates from Iberian Spain to the Canary Islands (1500 km) is around 99.5% (s = 4) and pollutes between 0.6 and 0.4 g CO 2 -eq /MJ of fuel (f = 4). Once the product arrives on the islands, the fuel is discharged and stored.Therefore, all emissions from this downstream process related to the movement and fuel processing are considered as indirect-local emissions (f = 5 to 5.3).The process efficiencies (s = 5.1 to 5.3) and the emissions of the diesel and fuel-oil storage were calculated.The process generally consumes steam to maintain adequate temperatures and conserve energy.The efficiencies in road transport are between 99.6% and 99.9% for diesel or gasoline and 99.5% and 99.7% for LPG [16].Finally, they arrive at the refueling stations to load the tanks of the ICEVs (completing the WtT path) or to the power plants to produce electricity (following in the next subsection).
|
<li> <b>vehicle:</b> Other Vehicle<li> <b>oil tankers:</b> Other Vehicle<li> <b>tanker:</b> Other Vehicle<li> <b>tankers:</b> Other Vehicle<li> <b>vessel:</b> Other Vehicle<li> <b>ICEVs:</b> Car
|
[
[
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 122
},
{
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"label": "vehicleType",
"start": 3891
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Oil tankers, tankers and vessels do not seem relevant for CCAM (at least we emphasize on ground automated mobility)\nConsequently, ICEV should be generally treated as a wheeled vehicle (\"Car\") in our case, but in this specific text it refers to vessels.\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
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{
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},
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{
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},
{
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"label": "vehicleType",
"start": 3891
}
] | null | null |
00769242-d4f1-48b6-b312-d8c61284cc56
|
completed
| 2025-04-09T16:14:38.079533 | 2025-05-23T21:18:09.874139 |
edd902c0-b21e-430f-a7ce-1a59ab9c151c
|
The rapidly increasing global populations and socio-economic development in the Global South have resulted in rising demand for natural resources. There are many plans for harvesting natural resources from the ocean floor, especially rare metals and minerals. However, if proper care is not taken, there is substantial potential for long-lasting and even irreversible physical and environmental impacts on the deep-sea ecosystems, including on biodiversity and ecosystem functioning. This paper reviews the literature on some potentials and risks to deep seabed mining (DSM), outlining its legal aspects and environmental impacts. It presents two case studies that describe the environmental risks related to this exploitative process. They include significant disturbance of the seabed, light and noise pollution, the creation of plumes, and negative impacts on the surface, benthic, and meso- and bathypelagic zones. The study suggests some of the issues interested companies should consider in preventing the potential physical and environmental damages DSM may cause. Sustainable mining and the use of minerals are vital in meeting various industrial demands.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
9c3ddd1b-a1d7-4795-a6b2-cdb5a1043bdf
|
completed
| 2025-04-09T16:14:38.079540 | 2025-05-26T13:48:17.715540 |
579b2edd-e4a7-4d47-9b1c-74507944b7a5
|
As the global population is forecasted to rise to almost 10 billion people by 2030 and 11 billion by 2050, the demand for minerals will follow a similar trend [1].In addition, the changing consumption patterns in the developing world will further impact the global demand for all resources, including minerals.There is a growing demand for strategic metals such as cobalt, nickel, copper, and manganese because their terrestrial reserves are fast depleting [2,3].Although some lab-based alloys can substitute minerals used by industries, this has limitations regarding capacity and quality, so mining activities will inevitably continue.Land-based mineral ores have been used to satisfy this demand in the past. However, several countries have started restricting mining activities using cheap techniques that show no concern for environmental or human health.As these countries try to prevent or even stop permitting these ill mining practices, coupled with the increase in demand, the interest to use deep seabed minerals has become more attractive [4].The Sustainable Development Goal (SDG) 12 targets clean manufacturing and the sustainable use of minerals to meet various industrial demands [5].At present, almost all mineral resources are extracted from terrestrial ore deposits.However, high-capacity and highquality ore deposits are becoming arduous to unearth, so the search expands to the deep seabed as an alternative for low-grade mining.Island countries occupy the deep-sea area within their territorial waters and Exclusive Economic Zones (EEZ), which is an area that sovereign states have special rights to explore and use its marine resources [6]. The deep seabed is generally an area 200 m below sea level.It largely lies outside the limits of coastal countries' continental shelf, defined as a section of a continent submerged beneath a shallow water area called a shelf sea.However, depending on the national coastline, it can also be within a continental shelf, especially in countries that have extended their EEZ.It is regulated by the 1982 UN Convention on the Law of the Sea (UNCLOS), called the "Constitution for the oceans" [6].This Convention is the basis for different rights and obligations concerning the oceans' uses, such as navigation, construction of pipelines and submarine cables, and national jurisdiction over coastal areas.Based on the Convention, institutions facilitating its implementation (the Commission on the Limits of the Continental Shelf, located in New York) and enforcement (The International Tribunal for the Law of the Sea located in Hamburg, Germany) were established.This legal system facilitates the peaceful settling of disputes and the protection of the oceanic environment and ecosystems. The deep seabed, also called "the area", is described in the Convention as "the seabed and ocean floor and subsoil thereof, beyond the limits of national jurisdiction."The Convention was signed by almost 200 countries, of which 168 have already ratified it, meaning that the obligations and rights established there apply to them [7].For the regions classified as continental shelves, which correspond to approximately 56% of the oceans, coastal states can develop their own rules.It is worth noting that state regulations are expected to be at least as strict as international regulations.The international seabed corresponds to approximately 44% of the oceans. The Convention was the basis for establishing the International Seabed Authority (ISA) or "the Authority."The Authority became operational in 1994 when the Convention came into force (12 months after its 60th ratification, according to article 308) [8].This institution aims to regulate activities in the deep seabed to prevent damage to ecosystems and biodiversity and even the economic advantages of seabed exploitation.The Authority has been working on a draft mining code to cover environmental, administrative, and financial aspects with a targeted deadline of 2020 for it to come into effect [8]. Article 136 of the Convention indicates that the main objective of the deep seabed mining (DSM) code is regulating the exploitation and development of mineral resources, which are the "common heritage of mankind" [7].The code means "the whole of the comprehensive set of rules, regulations, and procedures issued by ISA to regulate prospecting, exploration, and exploitation of marine minerals in the international seabed Area" [8].The Authority has issued 30 multi-year exploration permits, covering 1.3 million km 2 , or 0.7% of "the area". Those permits take the form of contracts, which establish the specific rights and obligations to the companies undertaking those activities.For a company to sign such a contract, it has to be supported by the ISA member of which the company is a national (article 4, of Annex III to the Convention).The supporting country then acts in the role of "sponsoring state".The sponsoring state is responsible for taking all necessary and appropriate measures to ensure that the sponsored companies comply with their contractual duties, with ISA regulations and with obligations arising from the Convention, such as protecting the marine environment and human life.Failure to take those measures means that those sponsoring states may also be liable for any damages which may occur [8]. However, no commercial mining activities so far occur, although DSM has been envisioned since the 1960s when the potential of extracting minerals and other resources from the seabed started to be noticed.This emergent industry took many years to develop due to three main factors:
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Correct"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
c91a8cfd-95ef-46a3-80f3-4ac0bbde3809
|
completed
| 2025-04-09T16:14:38.079546 | 2025-05-13T06:16:55.800230 |
34ced56c-f2b6-4708-bb71-2bb08e69c5b5
|
More and more attention become to Transport Company’s functioning efficiency due to growing of goods’ nomenclature and specific requirements for their service. Existing scientific and practical approaches to managing of transportation process consider separate service of each contract individually. Up-today requirements for transportation services complicate such evaluation. These requirements primarily include transportation frequency and volumes variation in each logistics system due to seasonal consumption of material flows. Different seasonality leads to irrational use of vehicles and decrees of their efficiency. All this gives rise to the mechanism of compatible transportation service of numerous logistics systems and their material flows by any enterprise. The paper consist of next sections the analysis of scientific framework and methods on the transportation services, fleet estimation, efficiency evaluations, analysis of requirements of transportation services; Mechanism of joint transportation services; Modelling of transportation services in logistics systems, where joint efficiency estimation of transportation functioning and logistics system and conclusions. The proposed methods and tools in the complex allow to identify and evaluate the effectiveness of the joint motor transport service of logistic systems by own and hired vehicles compared to the separate on the basis of performance indicators, which vary depending on the technological parameters: transport distances, runway usage factor, cargo class, load capacity of motor vehicles . The offered approach will reveal: regularities of change of indicators of efficiency of variants of the joint motor transport service between the traditional approach (a separate calculation of efficiency for each logistics system) and the proposed (calculation of compatible services), which allows to determine the equivalent cost of transport services during motor transport maintenance of material flows. The calculations confirm the effect of use compared to the separate combined transportation of material flows, which will be shown in reducing the required amount of vehicles by 31,8% and increasing efficiency from 5% to 60%, depending on the initial values of the transportation services parameters. The results of the project can be used in the formation of a freight vehicle fleet of any enterprise that is faced with the issue of hiring transport or have its own, PL providers, transport companies, and others.
|
<li> <b>vehicles:</b> Other Vehicle<li> <b>motor transport:</b> Other Vehicle<li> <b>motor vehicles:</b> Other Vehicle<li> <b>freight vehicle:</b> Truck
|
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[
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[
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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[
"\"Motor transport\" does not seem to be a vehicle type"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
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{
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"label": "vehicleType",
"start": 2346
}
] | null | null |
0530f3d2-326c-4fe7-9403-b57be74624dd
|
completed
| 2025-04-09T16:14:38.079552 | 2025-05-13T09:39:03.148544 |
19684e0a-e311-4f47-876a-6229fd5d3092
|
The capacity of an advection/diffusion model to predict sand transport under varying wave and current conditions is evaluated. The horizontal sand transport rate is computed by vertical integration of the suspended sediment flux. A correction procedure for the near-bed concentration is proposed so that model results are independent of the vertical resolution. The method can thus be implemented in regional models with operational applications. Simulating equilibrium sand transport rates, when erosion and deposition are balanced, requires a new empirical erosion law that involves the non-dimensional excess shear stress and a parameter that depends on the size of the sand grain. Comparison with several datasets and sediment transport formulae demonstrated the model’s capacity to simulate sand transport rates for a large range of current and wave conditions and sand diameters in the range 100–500 μm. Measured transport rates were predicted within a factor two in 67% of cases with current only and in 35% of cases with both waves and current. In comparison with the results obtained by Camenen and Larroudé (2003), who provided the same indicators for several practical transport rate formulations (whose means are respectively 72% and 37%), the proposed approach gives reasonable results. Before fitting a new erosion law to our model, classical erosion rate formulations were tested but led to poor comparisons with expected sediment transport rates. We suggest that classical erosion laws should be used with care in advection/diffusion models similar to ours, and that at least a full validation procedure for transport rates involving a range of sand diameters and hydrodynamic conditions should be carried out.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
2a8537fc-9051-4aa8-b049-833651619130
|
completed
| 2025-04-09T16:14:38.079559 | 2025-05-13T09:38:36.141884 |
7453eb3d-022b-4e3f-98bc-eb905286820c
|
Transporting sand in suspension is necessary in sediment transport models dealing with cohesive sediment, in order to allow the management of mixtures of sand and mud.The initial objective of this work was to evaluate the ability to predict fine to medium sand horizontal fluxes under varying wave and current conditions by using an advection-diffusion model.It has been shown that using classical erosion rate formulations from the literature in our model did not lead to consistent results.Those classical erosion laws should not be used in advection-diffusion models without a full validation procedure involving a range of sand diameters and hydrodynamic conditions.The ability to transport fine sands in our modelling framework was only made possible by introducing a new empirical erosion law.Further, the transport rate computed from the advection-diffusion model had to be independent of the vertical resolution, so that it can be implemented in regional models with operational applications.This was successfully achieved by using a correction procedure for extrapolating the near-bed concentration (in the deposition flux) from the concentration computed in the bottom layer.In addition, the actual horizontal flux in this bottom layer deserved a correction to account for very strong inverse gradients of velocity and concentration in the boundary layer.It is suggested that in a three-dimensional model, such a flux correction should be applied when solving the advection-diffusion equation for suspensions. Using the new empirical erosion flux formulation, the model demonstrated its capacity to simulate transport rates for sand diameter in the range 100-500 μm.The proposed erosion law was evaluated as best as possible for a wide range of different hydrodynamic conditions and grain sizes.This has been done despite the difficulty involved in qualifying sand transport models, illustrated by the very scattered results of classical sand transport formulations [19,41,51,60,62].This evaluation showed that our model compares well with various other sand transport models.While comparing the model with data, we showed that measured transport rates were predicted within a factor 2 in 67% of cases with current only and in 35% of cases with both waves and current.In comparison with the results obtained by Camenen and Larroudé [51], who provided the same indicators for several practical transport rate formulations (whose means are respectively 72% and 37%), our model gives reasonable results. Finally, the model presented in this study has been formulated for well-sorted, non-cohesive sediments.In the future, it would have to be adapted in the case of heterogeneous sediment composition to reach our longer term goal.where ρ is the water density, z is the elevation where the velocity u is computed in the first layer and κ is the Von Karman constant (0.4).Secondly, the wave induced shear stress is computed: where: -the wave friction factor fw is evaluated according to the Swart formulation [63]: and otherwise: , with T the wave period). The formulation of Soulsby [1] is used to account for non-linear wave-current interactions: and finally: where τm represents the mean (wave-averaged) shear stress in the direction of the current and φ is the angle between current and wave directions.τ is the maximum shear stress generated during a wave period and is either used for the expression of the skin sf or the hydraulic shear stress ff , depending on the roughness used (ks = kss or ksf).
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
b9b661bc-f32e-4e11-a1e7-fa9f298a8b07
|
completed
| 2025-04-09T16:14:38.079565 | 2025-05-19T09:40:08.627850 |
90923a38-eb3a-4b11-bfe0-96e43ca3512c
|
AbstractThe concentration of population in cities and processes of rural depopulation coupled with the generational shift to older societies represent new challenges in road safety. Here, we examine the severity of injuries suffered by the occupants of motor vehicles involved in a crash based on the population density of the area in which the crash occurs, the driver’s age and the density of their place of residence. We conduct the study in Spain, a country with one of the highest levels of elderly population concentrated in rural areas in Europe. Relational methods are used to match Eurostat’s urbanization classifications with the accident database of Spain’s Directorate General of Traffic so as to correlate each crash with the population density of the place where it occurred. A set of generalized linear models with random effects is fitted to analyze the relationship between population density and the bodily injury severity of the occupants of the vehicle(s) involved in a crash, measuring the effect of drivers’ relocation and aging by geographical area. Independence of injury severity and the degree of urbanization was rejected at the 5% significance level. While 53.8% of the Spanish population is living in densely populated areas and only 13.5% in rural areas, the latter concentrates most crashes with fatalities: 2.3 times more than in urban areas (43.5 and 18.6%, respectively). Drivers living in rural areas are more likely to be associated with serious or fatal injuries when involved in a crash in urban and intermediate areas. Moreover, drivers aged over 75 are significantly more likely to be associated with serious and fatal injuries, especially when the crash occurred in urban areas. Recent research alerts on the implications for rural (often elderly) residents of concentrating public services, particularly healthcare, in densely populated areas. Our study shows that motor crashes in more densely populated areas are also a rural health concern. Policy decision-makers need to address this issue to reduce the number of victims and their bodily injury severity.
|
<li> <b>motor vehicles:</b> Other Vehicle<li> <b>vehicle(s):</b> Other Vehicle
|
[
[
{
"end": 267,
"label": "vehicleType",
"start": 253
},
{
"end": 975,
"label": "vehicleType",
"start": 965
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 267,
"label": "vehicleType",
"start": 253
},
{
"end": 975,
"label": "vehicleType",
"start": 965
}
] | null | null |
84b90751-58cc-440f-9f08-f1137daa4516
|
completed
| 2025-04-09T16:14:38.079571 | 2025-05-26T06:50:21.951350 |
58a04ffe-a5c5-43aa-a49a-cd7b9c88c2aa
|
Many factors determine the risk of being injured in a road traffic crash and their interaction is complex.The generational shift towards older societies, the unequal spatial distribution of people and their different sociodemographic characteristics lead to new mobility needs of individuals.Responding to these mobility challenges is of concern to both governments and mobility stakeholders [29].The ongoing concentration of population in cities in conjunction with rural depopulation hinders the achievement of economies of scale outside cities [9], which continue to concentrate more facilities over time, while the provision of local services in low-density areas continues to fall [12,40,54].This trend is widespread throughout the EU, with some countries presenting a difference in rural and urban accessibility to services of more than 40 percentage points [27].Existing inequalities to access basic services are likely to be exacerbated, even if it runs counter to the Sustainable Development Goals of the 2030 Agenda [70,73]. As such, permanent rural residents have traditionally been forced to be more reliant on their private vehicles and to commute longer distances to urban centers where jobs, education and other services are concentrated [53].The higher dependence on the private vehicle of rural residents has implications in terms of road safety.Some studies, as Clark and Cushing [22], provide evidence that increased distance between people and/or medical facilities is a determinant of mortality from vehicle collisions.In fact, many studies have shown that the risk of crash depends on the distance driven [10,25] and the severity of traffic crashes is higher on low-density areas than in urban areas [6,56,75].In Spain, over 52% of all road traffic fatalities in 2021 occurred on rural roads [26]. Here, we seek to determine how the severity of the injuries suffered by victims of a motor vehicle crash differs in relation to the population density of the area in which the crash occurs.Our goal is to contextualize traffic crashes in the geographical area in which they take place, based on whether it has a low, medium, or high population density.We control for the population density of the driver's residence, given that this may differ from the population density of the place in which the crash occurred.To do so, an exhaustive exercise has first to be conducted to determine the location of each crash and the place of residence of the driver(s) and to assign to those places their corresponding population densities.In addition, we analyze the influence of other variables related to the crash, mainly the driver's age, but also some other characteristics of the vehicle, the crash, the occupants, and severity of the injuries.Ultimately, we wish to examine possible links between higher concentrations of the elderly in rural areas (resulting from decentralization) and the severity of bodily injuries incurred; yet, also, we seek to determine whether drivers from rural areas are more likely to be associated with serious or fatal injuries when involved in crashes in urban and intermediate areas. We focus here on the specific case of Spain, one of the countries in Europe with the highest level of population concentration in its cities [31], with the most aged population [59], and with the highest level of elderly population concentrated in rural areas [14,59].In so doing, we draw on the accident database of the Directorate General of Traffic (DGT), focusing on crashes that occurred between 2016 and 2019, and combine this with the Eurostat classification of the degree of urbanization of Spanish municipalities, to attribute a population density to each geographical location at which a crash occurred.Although we also dispose of accident data for 2020 crashes, we opted to exclude them because they reflect the consequences of the SARS-CoV-2 epidemic on mobility.In the analysis, we include a wide range of regressors, among which we highlight the age of the drivers segmented as follows: under 65, between 65 and 75, and over 75. 1 From a methodological perspective, therefore, we evaluate the severity of injuries suffered by occupants of a vehicle involved in motor vehicle crashes in Spain according to the degree of urbanization, using univariate and multivariate analyses, and in this sense each vehicle is our unit of analysis.For each vehicle, we classify its global bodily injury (BI) severity level according to the maximum BI severity observed for its occupants and, in this sense, we establish four categories: (i) non-BI damages, when none of the occupants suffers BI damages; (ii) slight BI damages, when the greatest severity suffered by the occupants is slight; (iii) severe BI damages, when the maximum category is serious and, (iv) fatal, when at least one occupant is killed.We use generalized linear mixed models (GLMMs), which include random effects to accommodate dependency between observations in the data set and so can include the different vehicles involved in a crash [62]. The analysis of driver longevity is not new.Researchers have highlighted that in numerous high-income countries, older drivers are disproportionately represented among the victims of road accident statistics [15,42,65,74].Many argue that the increased physical frailty of the elderly explains why they suffer worse crash outcomes [38,63,66], especially in older adults aged 75 years and above [2].Additionally, the consequences of a crash are more likely to be exacerbated by pre-existing health conditions [30,72].It has also been reported that the loss of visual and cognitive capacities among the elderly leads to impaired driving and increases their likelihood of being involved in a crash [23,50,61]. Other studies demonstrate that some older drivers are aware of their limitations [58] and self-regulate the number of kilometers they drive, either by reducing their exposure to challenging driving conditions, decreasing their overall mileage, changing how they drive or even ceasing to drive at all [3,46,47,51,60].Yet, the capacity of older drivers to self-regulate may be limited by their desire to maintain their lifestyle, the unavailability of family and friends to provide transport when required, or an unwillingness to ask them for help with transportation, and the lack of availability of public transport [5,7].There is evidence that driving cessation has a detrimental effect on the social and physical health of older adults [13,19,20,55].These factors may be particularly significant in rural areas. Keeping on mind that one of the main challenges to analyze the population density of the crash location is the definition of the geographical unit of analysis, the first relevant contribution of our research is to employ the European classification of the degree of urbanization of the local administrative units in conjunction with Spain's official traffic accident statistics in an attempt at correlating each crash with the population density where it occurred.Secondly, we model the relationship between population density and the severity of the crash, measuring the effect of relocation and aging by geographical area on the bodily injuries suffered by the victims. The rest of this paper is structured as follows.Section 2 presents the Eurostat methodology for determining the degree of urbanization of a municipality and its specific application to Spanish geography, as we seek to assign the correct population density to each place where a crash was recorded.In Sect. 3 we detail the criteria used to structure the micro databases provided by Spain's DGT and identify the variables we opt to maintain.Additionally, we present the methodology used to model the severity of bodily injuries suffered.The main results of the analysis are presented in Sect.4, both at a descriptive statistical level as well as for the binomial logistic regression with random effects modelling.We conclude the paper with a discussion of these findings and present our main conclusions.
|
<li> <b>private vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>motor vehicle:</b> Other Vehicle
|
[
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[
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[
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] |
[
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[
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] |
[
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] |
[
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[
"submitted"
] |
[
"different annotation between singular and plural [private vehicles vs private vehicle]"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
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{
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{
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{
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"label": "vehicleType",
"start": 4344
},
{
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"label": "vehicleType",
"start": 4385
},
{
"end": 1918,
"label": "vehicleType",
"start": 1905
},
{
"end": 4218,
"label": "vehicleType",
"start": 4205
}
] | null | null |
184bdccb-d17a-4d23-a9f2-aacea96b5cf1
|
completed
| 2025-04-09T16:14:38.079577 | 2025-05-26T14:26:28.807726 |
bc447ccb-76c1-4a15-89c1-fc0451256660
|
Cold orbital forging (COF) as an advanced incremental metal-forming technology has been widely used in processing vehicle parts. During the COF process, the vibration on the COF machine injures the service life of the machine and the quality of the forged part. The study of the vibration control of the COF machine is therefore necessary. In this study, the dynamic model of the COF machine is established, and the vibration performances of some key positions are obtained using Matlab&Simulink software. Subsequently, the vibration performances are effectively verified by conducting a vibration test experiment. Based on the dynamics model of the COF machine and Matlab&Simulink software, least-mean-squares (LMS), recursive least-squares (RLS) and OCTAVE vibration-control algorithms are applied to reduce the vibration. Comparing the vibration performances of the COF machine, these vibration-control algorithms are useful for reducing the vibration of the machine, which improves the service life of the machine and the quality of the forged part. Based on the vibration performances of the COF machine, the effects of LMS and RLS vibration controls are better than the OCTAVE, and they also obviously reduce the vibration of the COF machine. The vibration-control algorithms are first to be applied to reduce the vibration of the COF machines in this study, which will be beneficial to future research on the vibration controls of metal-forming machines and other mechanical systems.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
806ae12a-c45a-4976-9215-4525c4ffa715
|
completed
| 2025-04-09T16:14:38.079583 | 2025-05-13T09:38:31.267771 |
1412d81f-4fe4-40aa-9afc-6c5af10bb3b1
|
The structure of a T630 COF machine is shown in Figure 1, and the dynamics model is shown in Figure 2. Based on the generalized dissipation Lagrange principle and dynamics model of the T630 COF machine, the dynamics equation of the T630 COF machine is obtained as follows: x 32 + c 1 . where m 1 is the mass of the swing shaft (kg), m 2 is the total mass of the lower die and lower table (kg), m 3 is the mass of the frame (kg), x 13 is the relative vertical displacement of the swing shaft with respect to the frame (m), x 23 is the relative vertical displacement of the lower die with respect to the frame (m), x 32 is the relative vertical displacement of the frame with respect to the lower die (m), x 31 is the relative vertical displacement of the frame with respect to the swing shaft (m), P is the COF force (N), T is the vertical external excitation on the swing shaft, k 1 is the equivalent stiffness between the swing shaft and swing shaft bearing (N/m), c 1 is the equivalent damping between the swing shaft and swing shaft bearing (N•S/m), k 2 is the equivalent stiffness between the lower table and frame (N/m), c 2 is the equivalent damping between the lower table and frame (N•S/m), k 3 is the equivalent stiffness between the frame and foundation (N/m) and c 3 is the equivalent damping between the frame and foundation (N•S/m).Based on the generalized dissipation Lagrange principle and dynamics model of the T630 COF machine, the dynamics equation of the T630 COF machine is obtained as follows: According to the kinematics relationship, x 13 , x 23 , x 32 and x 31 can be obtained as follows: where x 1 is the vertical displacement of the swing shaft (m), x 2 is the vertical displacement of the lower die (m) and x 3 is the vertical displacement of the frame (m). Introducing Equation (2) into Equation ( 1), the dynamics equation of the T630 COF machine can be presented as Equation ( 3): Based on the former study [12], P can be obtained by parameters of the COF machine, and T can be obtained by the measurement (T = 81,267sin(2π × 4t)).The parameters of the COF machine (and the relevant parameters in Equation ( 3)) are shown in Table 1.Based on the parameters of the COF machine, Matlab&Simulink software (MATLAB 2020b) is used to solve the dynamics equation, and the dynamics Simulink model is established in Figure 3. Based on the oscilloscope results in the dynamics Simulink model, vertical accelerations of the swing shaft, the lower die and the frame are obtained in Figure 4.The amplitudes of these accelerations are 1.97 m/s 2 , 0.82 m/s 2 and 0.12 m/s 2 , respectively.The swing shaft contacts the product directly, and the vibration on the swing shaft is fierce, which is detrimental to the quality of the product.The vibration controls of the COF machine are proposed in the later section. To verify the effectiveness of oscilloscope results, an acceleration test experiment of the COF machine is conducted, and the experiment instruments are shown in Figure 5. The experimental acceleration of the test point (swing shaft) is presented in Figure 6.The amplitude of the experimental acceleration of the swing shaft is 1.99 m/s 2 , which is close to the above oscilloscope result (1.97 m/s 2 ).The oscilloscope result is effectively verified.Based on the oscilloscope results in the dynamics Simulink model, vertical accelerations of the swing shaft, the lower die and the frame are obtained in Figure 4.The amplitudes of these accelerations are 1.97 m/s 2 , 0.82 m/s 2 and 0.12 m/s 2 , respectively.The swing shaft contacts the product directly, and the vibration on the swing shaft is fierce, which is detrimental to the quality of the product.The vibration controls of the COF machine are proposed in the later section.To verify the effectiveness of oscilloscope results, an acceleration test experiment of the COF machine is conducted, and the experiment instruments are shown in Figure 5.The experimental acceleration of the test point (swing shaft) is presented in Figure 6.The amplitude of the experimental acceleration of the swing shaft is 1.99 m/s 2 , which is close to the above oscilloscope result (1.97 m/s 2 ).The oscilloscope result is effectively verified.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
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"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
bdeba2eb-fdc1-4b26-a29a-72b163c93734
|
completed
| 2025-04-09T16:14:38.079589 | 2025-05-26T08:33:09.082158 |
70157219-2fdb-48d2-8420-d65731c402de
|
This study aimed at investigating how driver's mental workload could be assessed during driving, using driving performance as well as electrophysiological and subjective data. Participants had to follow a lead vehicle at a safe and constant distance and to deal with two particular driving events (overtaking and pedestrian occurrence) within two sessions (baseline and experimental) on a driving simulator. Traffic density and time pressure (overtaking event) and time pressure (pedestrian event) were increased in the experimental session in order to induce a higher workload. Participants filled NASA TLX questionnaire after each driving session. Electrophysiological parameters (SCL, ECG), driving performance (SDLP and response to speed change of the lead vehicle: coherence, delay and gain) were analysed after each event in two temporal windows (30 sec and 5 min). Results showed that both performance and physiological variables differed as a function of traffic conditions and time pressure. Moreover, while performance variations were systematically observed over a long period (five minutes after the events), effects on mean SCL data obtained from experimental session notably differed from baseline values within thirty seconds after the events. Results are discussed in term of mental workload and suggestions are made about the safety systems that could monitor driver's mental state.
|
<li> <b>lead vehicle:</b> Other Vehicle<li> <b>overtaking:</b> Other Scenario<li> <b>pedestrian:</b> Pedestrian<li> <b>SCL:</b> Other Sensor<li> <b>ECG:</b> Other Sensor
|
[
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{
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]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"SCL\" and \"ECG\" are not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
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{
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},
{
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"label": "sensorType",
"start": 688
}
] | null | null |
d50bdd89-1138-4c1f-ab81-c086468c5b6b
|
completed
| 2025-04-09T16:14:38.079596 | 2025-05-26T13:24:06.137475 |
6745e663-a5e1-487a-a9c8-c26da7893f37
|
A pedestrian suddenly appeared on the right side of the road and crossed the street in front of the participants. In both baseline and experimental sessions, the pedestrian was hidden by a truck or a bus shelter (50% of occurrence).To avoid any learning effect and maximise the surprise effect, bus shelters were randomly placed along the circuit. -In the baseline session, the pedestrian started to cross the road when the participant's vehicle was 4 s from him (Time to collision, TTC = 4 s).-In the experimental pedestrian session, a TTC of 2 s was used. Participants came twice at the lab to perform the two driving sessions (1 week delay between each session).They were equipped with the ECG and SCL sensors, trained to the commands of the driving simulator, and then performed one of the two sessions. In short, each driving session included two driving events (overtaking or pedestrian), each event was followed by a 5 min of simple following task, then by the filling of the NASA TLX questionnaire and a 5 min break.
|
<li> <b>pedestrian:</b> Pedestrian<li> <b>truck:</b> Truck<li> <b>bus:</b> Bus<li> <b>vehicle:</b> Other Vehicle<li> <b>ECG:</b> Other Sensor<li> <b>SCL sensors:</b> Other Sensor
|
[
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] |
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[
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[
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] |
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"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
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"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
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"submitted"
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[
null
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[
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{
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"start": 200
},
{
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},
{
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{
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},
{
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"label": "sensorType",
"start": 701
}
] | null | null |
32fab3e5-c706-40fb-b67c-53c6246f8e91
|
completed
| 2025-04-09T16:14:38.079602 | 2025-05-26T14:10:39.231531 |
4978e19f-aca5-4bdc-bf9a-f453c10b5862
|
This paper proposes a framework for generating a battery test profile that accounts for the complex operating conditions of electric vehicles, which is essential for ensuring the durability and safety of the battery system used in these vehicles. Additionally, such a test profile could potentially accelerate the development of electric vehicles. To achieve this objective, the study utilizes a simplified longitudinal dynamics model that incorporates various factors such as the drivetrain efficiency, battery system energy conversion efficiency, and regenerative braking efficiency. The battery test profile is based on the China light-duty vehicle test cycle-passenger car (CLTC-P) and is validated through testing on an electric vehicle with a chassis dynamometer. The results indicate a high degree of consistency between the generated and measured profiles, confirming the efficacy of the simplified longitudinal dynamics model.
|
<li> <b>electric vehicles:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
[
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] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
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] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"electric vehicles\" could be other vehicles than cars"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
"end": 346,
"label": "vehicleType",
"start": 329
},
{
"end": 141,
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},
{
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},
{
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},
{
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"label": "vehicleType",
"start": 644
},
{
"end": 741,
"label": "vehicleType",
"start": 734
}
] | null | null |
082ff39c-7735-47c4-827e-101ccf519f2e
|
completed
| 2025-04-09T16:14:38.079608 | 2025-05-19T11:36:28.831564 |
7a559320-3010-4c80-9aa3-712136973392
|
The Internet of Things (IoT) has come of age, and complex solutions can now be implemented seamlessly within urban governance and management frameworks and processes. For cities, growing rates of car ownership are rendering parking availability a challenge and lowering the quality of life through increased carbon emissions. The development of smart parking solutions is thus necessary to reduce the time spent looking for parking and to reduce greenhouse gas emissions. The principal role of this research paper is to analyze smart parking solutions from a technical perspective, underlining the systems and sensors that are available, as documented in the literature. The review seeks to provide comprehensive insights into the building of smart parking solutions. A holistic survey of the current state of smart parking systems should incorporate the classification of such systems as big vehicular detection technologies. Finally, communication modules are presented with clarity.
|
<li> <b>car:</b> Car<li> <b>smart parking:</b> Automated Parking<li> <b>sensors:</b> Other Sensor
|
[
[
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{
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] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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},
{
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},
{
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"label": "scenarioType",
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},
{
"end": 617,
"label": "sensorType",
"start": 610
}
] | null | null |
7e059515-be9c-4210-a41a-84eb1ad08df9
|
completed
| 2025-04-09T16:14:38.079614 | 2025-05-20T04:47:41.286180 |
1c8e709a-8eae-4cf7-bbd3-8266f3dbff9d
|
Vehicle creation has developed impressively over the past 30 years, as discussed in [58].More vehicles on the roads causes more fuel and time utilization and a developing interest in parking spots.These issues can be tended to by advanced stopping arrangements, which are perhaps the most well-known use cases in the concept of the smart city and are employed to improve the quality of the life pattern of a city [59]. The engineering of advanced stopping arrangements is chiefly addressed by three components: sensors, organizing conventions, and programming arrangements.Sensors are the main component as they gather data and feed the entire framework.Systems administration conventions are represented by an entryway that carries out remote IoT conventions and interfaces sensors to the product frameworks.Finally, programming arrangements guarantee that data are accessible to all users through some kind of administration.For example, individuals can utilize these data to observe heat guides of zones with the most elevated stopping space inhabitance [60]. To carry out an advanced stopping arrangement, a few innovative segments are included, such as sensors, an organizing framework, and programming arrangements.With respect to stopping models, there are a few works that have been introduced by industry and established researchers.Some of them are centered around arrangement, while others focus on the calculations, programming, or frameworks, and some works discuss the innovation of the sensors.For example, the creators of [61] propose a methodology dependent on computerized reasoning (specialists) to identify accessible spots.In [62], the authors examine various ways to carry out smart parking arrangements, and they consider the entire environment of such kinds of arrangements, which essentially includes sensors, door choice, edge preparing, and server farm examination.Furthermore, the authors of [63] portray a design that is completely dependent on ZigBee innovation.In addition, the authors of [64] recommend man-made consciousness for advancing park search; however, they do not determine the specialized subtleties of execution, for example, explicit conventions or sensor types.Moreover, the work proposed in [65] shows the utilization of Bluetooth low energy (BLE) as a convention for associating sensors and passages.Bluetooth is a remote convention that upholds the association between end-gadgets.The BLE rendition does not burn through much energy and is important for remote IoT stack convention.Different arrangements, similar to those in [66], propose the utilization of IR sensors for engineering.Cell phones are likewise thought to be in these arrangements, especially to discover accessible spaces.Considering the previously mentioned study, it can be seen that there are no global norms or base models characterized for the execution of smart stopping frameworks.Along these lines, it is just as important to examine how various parts are being utilized as it is to distinguish propensities concerning their utilization in order to carry out a smart stopping arrangement. Advanced stopping arrangements were created with numerous innovations and approaches; subsequently, a grouping was performed on the basis of the setup focuses.For this situation, three alternate points of view were chosen: sensors, the network framework, and administration given to clients.The previously mentioned points of view were chosen depending on the significance given in [67].
|
<li> <b>Vehicle:</b> Other Vehicle<li> <b>vehicles:</b> Other Vehicle<li> <b>sensors:</b> Other Sensor<li> <b>smart parking:</b> Automated Parking<li> <b>IR sensors:</b> Other Sensor
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] | null | null |
75baa437-b82c-4fdd-98c3-902f20030962
|
completed
| 2025-04-09T16:14:38.079620 | 2025-05-19T09:42:14.730683 |
aa5b15b7-e52d-4b55-a0a9-1067365bb6bd
|
Road accidents have a relevant impact in terms of economic and social costs. As a consequence, many research studies have focused on identifying the key factors affecting accident severity. Traditionally, these factors can be included in the infrastructural, human and vehicle groups. Among these, human factors have a relevant impact on accident severity, which depends on driving experience, driver’s socio-economic characteristics, and driving behaviour, but also on the driver’s psychological state while driving. In this paper we investigate on the relationships between driving behaviour usually taken by the driver and his/her perceived psychological state while driving. In order to achieve this goal we adopt an Ordered Probit (OP) model formulation calibrated on the basis of experimental data collected by a sample survey. We demonstrate that the adopted methodology accounts for the differential impacts of certain human factors on driver’s psychological state.
|
<li> <b>vehicle:</b> Other Vehicle
|
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[
null
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
{
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] | null | null |
203ce8d6-7bec-47f2-8837-c2d4449ce2ae
|
completed
| 2025-04-09T16:14:38.079626 | 2025-05-26T08:00:34.767403 |
543fde4c-6999-4d28-a913-f9b8482bef79
|
In this work, OP model methodology was used to investigate the factors affecting the psychological state while driving.OP model was chosen to carry out the analysis because it is a flexible model and allows the psychological state probability to vary differently across categories, based on the explanatory variable. The analysis was based on data collected by a faceto-face survey conducted in the urban area of Cosenza (in the South of Italy).The model was specified by considering 'psychological state while driving' as dependent variable, whereas the explanatory variables were selected on the basis of the analysis of the theoretical reasoning linking with the dependent variable.The explanatory variables are driving behaviour variables, such as making other activities connected to guide, using mobile phone while driving, driving after an alcoholic drink, driving in not optimal conditions, using safety belts, respecting safety distance, speed limits, and overtaking rules; and socio-economic characteristics, as driver's age and gender. The results of the model appear congruent with the statistics about the driving behaviour, suggesting some considerations reported in the following: 1.Several factors play major roles in affecting the psychological state while driving.Among these, the respect of overtaking rules and speed limits have a considerable impact on the driver's psychological state, as well as bad habits such as making other activities connected to guide while driving, or driving in not optimal psychological conditions.As expected, the respect of the rules is indicative of a relatively careful psychological state, while driving bad habits are peculiar to aggressive drivers.2. People in general adopt a safe driving behaviour, but there is a not insignificant part of drivers who are impatient and adopt dangerous and risky manners while driving.This part of users represents potential causes of road accidents, and consequently of serious injuries in terms of economic and social costs.The results of the statistics, as well as the results of the model, suggest that there is a certain correlation between driving behaviour and habits and the psychological state of the drivers.Being psychological state affected by driving behaviour, just psychological state can influence the happening of a road accident and its severity.3. Investigating on the human factors affecting road accident severity and specifically on the complex interaction between driver's behaviour and accident risk is very important.The application of OP models to investigate the impacts (driving behaviour, compliance to the road rules, and drivers' characteristics) on driver's psychological state can provide interesting implications in the study of human factors affecting accident severity.To this end, our findings can represent a useful contribution to the scientific literature of the sector, and can have relevant implications in road safety decisions and policy.
|
None
|
[
[]
] |
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"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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"submitted"
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"Correct"
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null
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"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[] | null | null |
e5652df8-cd51-4100-9f59-a52ffb8f4d5d
|
completed
| 2025-04-09T16:14:38.079632 | 2025-05-20T14:27:51.945058 |
868b33b3-2e81-47dc-9584-ab6994d99f3e
|
The advancement of SAE Level 3 automated driving systems requires best practices to guide the development process. In the past, the Code of Practice for the Design and Evaluation of ADAS served this role for SAE Level 1 and 2 systems. The challenges of Level 3 automation make it necessary to create a new Code of Practice for automated driving (CoP-AD) as part of the public-funded European project L3Pilot. It provides the developer with a comprehensive guideline on how to design and test automated driving functions, with a focus on highway driving and parking. A variety of areas such as Functional Safety, Cybersecurity, Ethics, and finally the Human–Vehicle Integration are part of it. This paper focuses on the latter, the Human Factors aspects addressed in the CoP-AD. The process of gathering the topics for this category is outlined in the body of the paper. Thorough literature reviews and workshops were part of it. A summary is given on the draft content of the CoP-AD Human–Vehicle Integration topics. This includes general Human Factors related guidelines as well as Mode Awareness, Trust, and Misuse. Driver Monitoring is highlighted as well, together with the topic of Controllability and the execution of Customer Clinics. Furthermore, the Training and Variability of Users is included. Finally, the application of the CoP-AD in the development process for Human-Vehicle Integration is illustrated.
|
<li> <b>SAE Level 3 automated driving systems:</b> Level 3<li> <b>SAE Level 1:</b> Level 1<li> <b>Level 3 automation:</b> Level 3<li> <b>automated driving:</b> Other Level of Automation<li> <b>parking:</b> Automated Parking<li> <b>SAE Level 3 automated driving systems:</b> Level 3<li> <b>SAE Level 1:</b> Level 1<li> <b>Level 3 automation:</b> Level 3<li> <b>automated driving:</b> Other Level of Automation<li> <b>parking:</b> Automated Parking
|
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] | null | null |
4612a600-0569-48a6-bfb9-0bccbb7cb54f
|
completed
| 2025-04-09T16:14:38.079638 | 2025-05-26T08:16:24.796286 |
34cd403a-0e4a-4556-ade0-0024cedc1617
|
The topic or process poses a common challenge in the development process that requires cooperation. A wrongly applied approach for the topic or process would lead to serious consequences (e.g., malfunctions in certain traffic situations leading to non-release of the function). A frequent misapplication of an approach for a topic or process is highly likely. The topic/process has already been identified as relevant by others. The topic or process can be described in a general way that does not lead to unreasonable limitations in the development process (company independent).And the optional criteria: the topic or process is of relevance for L3Pilot prototype vehicles and can be evaluated in this project. With regard to the actual process of applying the CoP-AD, the decision was made to use the existing Code of Practice for Advanced Driver Assistance Systems as a baseline.Figure 1 shows the selected development phases for the CoP-AD.Compared to the Code of Practice for Advanced Driver Assistance Systems, the number of phases was reduced from six to four during the actual development.The second and fourth phase originally consisted of two separate stages, but these were condensed into the Concept Selection Phase and the Validation and Verification Phase for greater simplicity.An additional phase for the time post start of production was added to cover the entire lifecycle of the ADF.The conceptual stage consists of the Definition Phase and Concept Selection Phase, while the Design Phase and the Validation and Verification Phase constitute the series development stage.During the Definition Phase, the basic requirements are defined and based on this, the best concept is chosen in the Concept Selection Phase.The Design Phase requires the detailed design of the system.Then, it is validated and verified in the final phase before the start of production.Post start of production, further data can be gathered and improvements can be applied.This process is not necessarily linear; iterative improvements with repetitions of important steps might be possible.The process has been designed to remain abstract on purpose, so that the CoP-AD can be applied to the many different development processes in place in the industry at various companies. Information 2020, 11, 284 3 of 13 possible.The process has been designed to remain abstract on purpose, so that the CoP-AD can be applied to the many different development processes in place in the industry at various companies.In order to clearly summarize the topics that were collected, a number of categories were defined to cluster them.Table 2 shows the categories finally chosen with the pertaining topics.They are based on extensive expert discussions, clustering all the available topics in a meaningful way.The last row on Human-Vehicle Integration is the key focus of this paper.The first category is quite generic and focusses on overall guidelines and recommendations, such as a minimal risk manoeuver.The Operational Design Domain (ODD) on the Vehicle Level offers a description of the function and scenarios at the level of the vehicle.The category ODD on the Traffic System Level, including Behavioral Design, offers a description of the function on the level of the overall environment and a description of the behavior of other road users.Safeguarding Automation is about how to ensure a safe operation of the function, primarily the functional safety, but also the cybersecurity and data privacy aspects.Human-Vehicle Integration is the interaction between the driver and the vehicle's displays and control elements. The topics within each of the categories were distributed along the development process phases in a workshop.In order to better address the topics derived from previously held expert sessions, a thorough literature review was done to back up the topics with research results and existing best practices.Based on this, the questions for the CoP-AD checklist were phrased.These questions underwent a rigorous iterative improvement process, improving overall quality and reducing the overall number of available questions to the most important ones.This enabled the deliverable D2.2In order to clearly summarize the topics that were collected, a number of categories were defined to cluster them.Table 2 shows the categories finally chosen with the pertaining topics.They are based on extensive expert discussions, clustering all the available topics in a meaningful way.The last row on Human-Vehicle Integration is the key focus of this paper.The first category is quite generic and focusses on overall guidelines and recommendations, such as a minimal risk manoeuver.The Operational Design Domain (ODD) on the Vehicle Level offers a description of the function and scenarios at the level of the vehicle.The category ODD on the Traffic System Level, including Behavioral Design, offers a description of the function on the level of the overall environment and a description of the behavior of other road users.Safeguarding Automation is about how to ensure a safe operation of the function, primarily the functional safety, but also the cybersecurity and data privacy aspects.Human-Vehicle Integration is the interaction between the driver and the vehicle's displays and control elements. The topics within each of the categories were distributed along the development process phases in a workshop.In order to better address the topics derived from previously held expert sessions, a thorough literature review was done to back up the topics with research results and existing best practices.Based on this, the questions for the CoP-AD checklist were phrased.These questions underwent a rigorous iterative improvement process, improving overall quality and reducing the overall number of available questions to the most important ones.This enabled the deliverable D2.2 [3] to be written, which is a draft used to gather feedback from external partners outside the L3Pilot consortium.This will culminate in the deliverable D2.3, the final CoP-AD, to be presented in 2021. In order to apply the CoP-AD appropriately, a template was defined for all questions; this can be seen in Table 3.The reference number for each question can be found in the top left cell of the table, and the development phases associated with the question have been marked in the top right.In the body of the table, the main question is on the left, supported where applicable by sub-questions on the right.Only the main question needs to be answered directly with yes or no.Ideally, independent evaluators (e.g., individuals from other departments or external sources such as research institutes) who have formal training or experience in the subject matter of the topics are also involved in the application of the CoP-AD.For example, for the Human-Vehicle Integration topic, the evaluator should have experience in human factors, usability engineering, or cognitive ergonomics. Following the CoP means that all of the questions should be answered positively, or, that the issue raised by the items has been solved in another way.The sub-questions serve as an elaboration.The main question is phrased in a way that an answer with yes always means that the question has been addressed sufficiently.However, even in case a no is given as an answer, this may still be appropriate, as there might be good reasons why something could not be done or answered, or is simply not applicable in a given case, as long as the underlying problem is solved and documented.For some of the items, accepted pass/fail criteria are available (such as the number of participants that need to pass a controllability confirmation test), others are relying on norms (e.g., legibility of displays) or expert assessments if these kinds of thresholds are not available.In a further step, the questions may be transferred to an Excel file or another software tool for easy application and editing. The CoP-AD was scoped to cover motorway and parking scenarios for SAE level 3 and level 4 functions.Although only EU markets are currently in scope, it is assumed that the CoP-AD may also be applied to non-EU regions, as well as urban or rural traffic scenarios, and even driverless robot taxis.This needs to be investigated in further research. In the third section of this paper, the HVI category is explained in detail.This also includes examples of the questions asked.
|
<li> <b>L3Pilot prototype vehicles:</b> Other Vehicle<li> <b>Vehicle:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>other road users:</b> Other VRU<li> <b>motorway:</b> Other Scenario<li> <b>parking scenarios:</b> Automated Parking<li> <b>SAE level 3:</b> Level 3<li> <b>level 4:</b> Level 4<li> <b>urban or rural traffic scenarios:</b> Other Scenario<li> <b>robot taxis:</b> Other Vehicle<li> <b>L3Pilot prototype vehicles:</b> Other Vehicle<li> <b>Vehicle:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>other road users:</b> Other VRU<li> <b>motorway:</b> Other Scenario<li> <b>parking scenarios:</b> Automated Parking<li> <b>SAE level 3:</b> Level 3<li> <b>level 4:</b> Level 4<li> <b>urban or rural traffic scenarios:</b> Other Scenario<li> <b>robot taxis:</b> Other Vehicle
|
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},
{
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"label": "levelOfAutomation",
"start": 8028
},
{
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"label": "scenarioType",
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},
{
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},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
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},
{
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"start": 3025
},
{
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},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 4712
},
{
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"label": "vehicleType",
"start": 5183
},
{
"end": 6831,
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},
{
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"label": "vehicleType",
"start": 3110
},
{
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"label": "vehicleType",
"start": 3562
},
{
"end": 4804,
"label": "vehicleType",
"start": 4797
},
{
"end": 5256,
"label": "vehicleType",
"start": 5249
},
{
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"label": "VRUType",
"start": 3307
},
{
"end": 5010,
"label": "VRUType",
"start": 4994
},
{
"end": 7985,
"label": "scenarioType",
"start": 7977
},
{
"end": 8007,
"label": "scenarioType",
"start": 7990
},
{
"end": 8023,
"label": "levelOfAutomation",
"start": 8012
},
{
"end": 8035,
"label": "levelOfAutomation",
"start": 8028
},
{
"end": 8207,
"label": "scenarioType",
"start": 8175
},
{
"end": 8240,
"label": "vehicleType",
"start": 8229
}
] | null | null |
c8923651-3ce3-4503-811b-7628e01e7fac
|
completed
| 2025-04-09T16:14:38.079644 | 2025-05-26T13:26:05.227920 |
bba36ef5-b930-48e1-952c-3044efb6aaa4
|
The pharmaceutical cold chain (PCC) deals with specific logistics operational require-ments related to product quality, safety, and regulations that make the supply chain management process complex. Also, the pharma industry market growth increases the awareness, in terms of good's temperature monitoring and controlling, of the storage and transportation processes across the network. This study provides a novel approach to PCC, based on a systematic literature review with an extensive analysis of the main aspects that influence the supply chain processes. The major findings highlight the recently worldwide research progress on the PCC subjects related, the challenges involving the PCC and its associated technological advances based on three attributes (product characteristics, vehicle capabilities, and logistics service provider’s expertise) and, finally, the impact of technologies and its potential utilization to im-prove the decision-making process on integrated cold chain operations.
|
<li> <b>vehicle capabilities:</b> Other Vehicle<li> <b>vehicle capabilities:</b> Other Vehicle
|
[
[
{
"end": 795,
"label": "vehicleType",
"start": 788
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"deleted \"capabilities\" from the vehicleType phrase \"vehicle capabilities\" leaving just the term \"vehicle\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 808,
"label": "vehicleType",
"start": 788
},
{
"end": 808,
"label": "vehicleType",
"start": 788
}
] | null | null |
7ac5bcfc-2767-4813-92db-c92b5095bdff
|
completed
| 2025-04-09T16:14:38.079650 | 2025-05-26T07:35:35.842678 |
30a9f700-9a06-46b0-a71d-ffd6d80bc652
|
Conversion of a chemical into its optical or geometric isomer, having different pharmacological or toxicological activity.(hereoptically active substance looses its optical activity without change in chemical composition) .biologicalactivity of the formulations is hampered as for e.g.biological effect of a drug in dextro form can be less than that in laevo form. Adrenaline has optical 15-20 times greater biological activity then D -Adrenaline.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
4e683228-60b6-41e9-982f-e107df47a168
|
completed
| 2025-04-09T16:14:38.079656 | 2025-05-26T08:41:15.622923 |
36f9e31f-8110-4c2a-9e19-393e8be1753a
|
The underwater environment is characterized by hazardous conditions that make it difficult to manage and monitor even the simplest human operation. The introduction of a robot companion with the task of supporting and monitoring the divers during their activities and operations underwater can help to solve some of the problems that usually arise in this scenario. In this context, a proper communication between the diver and the robot is imperative for the success of the dive. However, the underwater environment poses a set of technical challenges which are not readily surmountable thus limiting the spectrum from which possibilities can be chosen. This paper presents the design and development of a gesture-based communication language which has been employed for the entire duration of the European project CADDY (Cognitive Autonomous Diving Buddy). This language, the Caddian, was built upon consolidated and standardized underwater gestures that are commonly used in recreational and professional diving. Its use and integration during field tests with a remotely operated underwater vehicle (ROV) is also shown.
|
<li> <b>robot:</b> Other Vehicle<li> <b>remotely operated underwater vehicle (ROV):</b> Other Vehicle<li> <b>robot:</b> Other Vehicle<li> <b>remotely operated underwater vehicle (ROV):</b> Other Vehicle
|
[
[
{
"end": 175,
"label": "vehicleType",
"start": 170
},
{
"end": 437,
"label": "vehicleType",
"start": 432
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- \"remotely operated underwater vehicle (ROV)\" is not of interest to CCAM\n- Linked entities appear duplicate\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
"start": 170
},
{
"end": 437,
"label": "vehicleType",
"start": 432
},
{
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},
{
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},
{
"end": 437,
"label": "vehicleType",
"start": 432
},
{
"end": 1108,
"label": "vehicleType",
"start": 1066
}
] | null | null |
224c2ecc-ea67-4a86-b676-e5bbdfdb06b1
|
completed
| 2025-04-09T16:14:38.079662 | 2025-05-26T14:09:48.580905 |
02ebe01e-a7f4-4150-9cf4-21e1ec2becbc
|
"Take a photo" Missions-In this kind of mission, the diver commands the AUV to take a picture from the point where it is stationing.• "Do a mosaic" Missions-In this kind of mission, the diver commands the AUV to do a mosaic/tessellation of an area n x m of the seabed (see Section 3 under Works).
|
<li> <b>AUV:</b> Other Vehicle<li> <b>AUV:</b> Other Vehicle
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Incorrect"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"AUV\" not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 205
},
{
"end": 75,
"label": "vehicleType",
"start": 72
},
{
"end": 208,
"label": "vehicleType",
"start": 205
}
] | null | null |
604e75ed-8e65-48f6-8eca-0b7c4bfb8559
|
completed
| 2025-04-09T16:14:38.079668 | 2025-05-26T14:09:23.972512 |
bd7c018a-a46b-49b8-8bd5-158d20c1469b
|
This paper presents the estimation of nine types of utility function parameters for the application in EVA mode choice model for the city of Ljubljana, Slovenia. Four different modes (private car, public transport, bike and walking) and five purposes (work, education, shopping, leisure and other) were taken into consideration. This paper presents first the design of the Stated Preference survey, then a brief review of the EVA model, different types of utility functions and the estimation method. The final log-likelihood enables comparison of different types of utility functions. The results show that absolute differences in final log-likelihood among most types of utility functions are not high despite the different shapes, which implies that different functions may best describe different variables.
|
<li> <b>private car:</b> Car<li> <b>public transport:</b> Bus<li> <b>bike:</b> Cyclist<li> <b>walking:</b> Pedestrian<li> <b>private car:</b> Car<li> <b>public transport:</b> Bus<li> <b>bike:</b> Cyclist<li> <b>walking:</b> Pedestrian
|
[
[
{
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"label": "vehicleType",
"start": 184
},
{
"end": 219,
"label": "VRUType",
"start": 215
},
{
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"label": "VRUType",
"start": 224
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"- public transport is not bus exclusively\n- Linked entities appear duplicate\n"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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"label": "vehicleType",
"start": 197
},
{
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"start": 215
},
{
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},
{
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},
{
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"label": "vehicleType",
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},
{
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"label": "VRUType",
"start": 215
},
{
"end": 231,
"label": "VRUType",
"start": 224
}
] | null | null |
cc21c997-87c7-4bae-a40e-e8d94fcc32fd
|
completed
| 2025-04-09T16:14:38.079674 | 2025-05-13T06:53:01.810615 |
05f9cb2e-5d9e-4333-bded-ce15ad51a729
|
From 2,438 survey forms made, only 1,276 were used for calculating utility functions, as only those travellers with more alternatives and with willingness to use them were taken into account.Besides, data from situations were useful only if at least one of them referred to car or public transport since only parameter values for these two alternatives change in situations.Some basic information about survey performance is shown in the tables below. Table 3 shows that the number of surveys for each purpose was sufficient to enable a representative sample.The number of surveys made on the trips by bike (Table 3 andTable 4) was low because of cold weather conditions. Last column in Table 4 and Chart 1 show the modal split in the surveys.This modal split cannot be taken as actual modal split for several reasons: -The most important factor is the choice of locations, which directly affects the choice in the sample (e.g.: the more surveys made on trains, the greater share of public transport choice in the sample).-Car users and cyclists are relatively unready to use any alternative.-Relatively small sample to investigate modal split. -Only trips in progress were measured.
|
<li> <b>car:</b> Car<li> <b>public transport:</b> Bus<li> <b>bike:</b> Cyclist<li> <b>trains:</b> Other Vehicle<li> <b>cyclists:</b> Cyclist<li> <b>car:</b> Car<li> <b>public transport:</b> Bus<li> <b>bike:</b> Cyclist<li> <b>trains:</b> Other Vehicle<li> <b>cyclists:</b> Cyclist
|
[
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
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] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
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] |
[
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] |
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},
{
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"start": 602
},
{
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"label": "vehicleType",
"start": 954
},
{
"end": 1045,
"label": "VRUType",
"start": 1037
}
] | null | null |
e74221de-d51b-4826-afd9-71498e082d16
|
completed
| 2025-04-09T16:14:38.079680 | 2025-05-26T07:50:45.505595 |
3dbe72a8-9568-47fa-9e00-c129ded46d2c
|
The behaviour of traffic in the heterogeneous environment of an urban signalized intersection is complex and difficult to model. This paper presents the development of a simulation model to imitate the flow of heterogeneous traffic through a signalized intersection. It discusses the validation of the proposed model in terms of queue density and dissipation of vehicles at an intersection approach and found to be satisfactorily replicating the field conditions. In this study, the model was extended to examine the effects of left turn channelization on vehicle waiting times. Sensitivity analysis was carried out to study the variation of vehicle waiting times. Analysis estimated that vehicle waiting times were reduced if a channelization was provided for a high traffic volume and certain proportions of left turn vehicles in the intersection approach. The length of channelisation has marginal impacts on vehicle waiting times.
|
<li> <b>intersection:</b> Other Scenario<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>intersection:</b> Other Scenario<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
[
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}
]
] |
[
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] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"linked entities appear duplicate"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
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},
{
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"start": 820
},
{
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"start": 556
},
{
"end": 649,
"label": "vehicleType",
"start": 642
},
{
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"label": "vehicleType",
"start": 689
},
{
"end": 919,
"label": "vehicleType",
"start": 912
}
] | null | null |
370b95dd-8b19-4f3a-80af-ba4861b04a49
|
completed
| 2025-04-09T16:14:38.079686 | 2025-05-26T13:28:04.810709 |
8d55a041-404d-4478-9bda-63f6d122aaf2
|
Traffic movements were captured using videography during traffic peak hours.The data for one approach of the intersection (Kotturpuram road approach) was captured using a video camera.The camera was placed at an elevated position to capture the queue, dissipation of vehicles and longitudinal spacing of vehicles in stopped condition.The input parameters for the simulation model, such as traffic volume, proportion of turning movements, vehicle composition, longitudinal spacing and dissipation rate were extracted from the video data.A total traffic volume at the intersection approach was 3067 veh/h.Vehicular composition of traffic at a study stretch is shown in Fig. 6.There are totally 30 signal cycles for one hour.
|
<li> <b>intersection:</b> Other Scenario<li> <b>vehicles:</b> Other Vehicle<li> <b>camera:</b> Camera<li> <b>vehicle:</b> Other Vehicle<li> <b>intersection:</b> Other Scenario<li> <b>vehicles:</b> Other Vehicle<li> <b>camera:</b> Camera<li> <b>vehicle:</b> Other Vehicle
|
[
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{
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{
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{
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},
{
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},
{
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},
{
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},
{
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}
]
] |
[
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] |
[
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] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Linked entities appear duplicate"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
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},
{
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"label": "vehicleType",
"start": 304
},
{
"end": 183,
"label": "sensorType",
"start": 177
},
{
"end": 194,
"label": "sensorType",
"start": 188
},
{
"end": 445,
"label": "vehicleType",
"start": 438
}
] | null | null |
f04212ad-be82-4bfe-ab5e-bdab4584b81d
|
completed
| 2025-04-09T16:14:38.079692 | 2025-05-20T11:15:21.799676 |
84f4c680-1b92-492d-ad29-523822b44559
|
The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers' comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.
|
<li> <b>electric vehicles:</b> Car<li> <b>vehicle:</b> Other Vehicle
|
[
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"electric vehicles\" could be scooters or mini-vans, not cars exclusively"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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},
{
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{
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{
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"label": "vehicleType",
"start": 405
},
{
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"label": "vehicleType",
"start": 1026
}
] | null | null |
8dc5e8fa-4346-4cb6-95e8-69c712bc570a
|
completed
| 2025-04-09T16:14:38.079699 | 2025-05-20T10:58:35.443570 |
a24bc3b9-ebe2-4114-82e8-4b2ef5988c8b
|
This paper focuses on two system-wide objectives for the charging optimization of electric vehicles: (i) MIN-DEV and (ii) MIN-COST.The former aims at minimizing the standard deviation, σ , of the total demand as a measure of load uniformity, load balancing and peak-shaving that contribute to the reliability of the Smart Grid.The latter aims at reducing the total energy cost by taking into account temporal energy prices. Agents employ the I-EPOS system [11,12] as a fully decentralized and privacy-preserving learning mechanism for coordinating the charging of electric vehicles.I-EPOS has been studied earlier in load-balancing of bike sharing stations [12] and in demandresponse of residential energy consumption [31,32,41].In that Smart Grid scenario, the agents control individual home appliances or the aggregate demand of the In contrast, this paper contributes a new application of I-EPOS in Smart Grids and provides fundamental insights on how the charging of electric vehicles can be modeled as a 0-1 multiple-choice combinatorial optimization problem.In such a model, the optimization turns to be a plan selection problem of complexity O(v n ): each agents selects one of the possible charging plans such that the total consumption of all electric vehicles satisfy one of the two aforementioned objectives. To manage this vast combinatorial complexity, the agents of I-EPOS are self-organized [55] in a tree topology as a way to structure their interactions with which they perform a cooperative optimization.A tree topology is a design choice to perform a computationally cost-effective aggregation of the power demand level as well as to perform coordinated decision-making.The computational and communication complexity depends on the number and size of plans v as well as the number of children k that each node has such that O(v k ), while the network topology does not have a significant impact on the performance as earlier shown [12].This makes I-EPOS a highly efficient and scalable distributed algorithm for problems of combinatorial complexity as confirmed with comparisons to other state of the art algorithms [12]. The optimization of the plan selections is performed by a set of consecutive learning iterations of bottom-up (leaves to root) and top-down (root to leaves) interactions.At each iteration, an agent i selects a plan j to satisfy the MIN-DEV optimization objective as follows: where σ () measures the standard deviation of a plan, â1 = ∑ n i=1 di is the earlier aggregate plan of all earlier selected plans di summed up at the root i = 1, âi , a i are the earlier and current aggregate plans respectively of all plan selections of the agents in the branch underneath agent i and di , d i,j are the earlier selected plan and the current possible plan j of agent i.Note that the minimization of variance and standard deviation are quadratic cost functions [10] that requires coordination among the agents' selections.The aggregate plans in Eq. ( 6) serve this purpose.Moreover, privacy is preserved by only exchanging aggregate plans instead of the individual ones.Further elaboration on the I-EPOS algorithm is out of the scope of this paper and is available on earlier work [11]. The MIN-COST selection function aims at reducing the total energy cost by taking into account the temporal energy prices as follows: where a i,t is the power demand of the aggregate plan at time t, d i,j,t is the power demand of the possible plan j at time point t and p t is the energy price at time t.Note that this is a linear cost function that can be minimized locally without requiring coordination among agents, i.e. the minimum total energy cost computed at the end of the first learning iteration is optimal and therefore no further iterations are required.The use of I-EPOS in this case serves exclusively the distributed aggregation of the selected plans a i and therefore, the term a i,t is not actually required for the optimization. This paper studies how the optimization of reliability using the MIN-DEV and MIN-COST objectives may influence human and social aspects such as the discomfort and fairness respectively.The system discomfort G d is measured by the average discomfort as follows: A set of charging regimes are defined as fair if all agents have the same level of discomfort.Fairness increases with the reduction in the dispersion of discomfort.Mathematically, fairness is defined as follows: where σ (g 1 , . . ., g n ) measures the standard deviation of the discomfort values among the agents. Note that other more complex objective functions for reliability could be employed such as scenarios of power generator failures [41] and cascading failures triggered by power line failures [53].In such scenarios I-EPOS can optimize a matching objective between power demand and a given incentive signal computed by parametric power supply models of transactive control systems [41].Such models are made available within the I-EPOS software artifact [12].
|
<li> <b>electric vehicles:</b> Car<li> <b>bike:</b> Cyclist
|
[
[
{
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},
{
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"label": "vehicleType",
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},
{
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{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 99,
"label": "vehicleType",
"start": 82
},
{
"end": 581,
"label": "vehicleType",
"start": 564
},
{
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},
{
"end": 1269,
"label": "vehicleType",
"start": 1252
},
{
"end": 639,
"label": "VRUType",
"start": 635
}
] | null | null |
b0ab92c1-d53b-4228-9d78-d444df2cab36
|
completed
| 2025-04-09T16:14:38.079706 | 2025-05-20T11:17:06.379279 |
d8835ed3-940e-4a56-8f6c-91bce1b73c65
|
In this study, the traffic parameters were collected from three work zones in Iran in order to evaluate the queue length in the work zones. The work zones were observed at peak and non-peak hours. The results showed that abrupt changes in Freeway Free Speed (FFS) and arrival flow rate caused shockwaves and created a bottleneck in that section of the freeway. In addition, acceleration reduction, abrupt change in the shockwave speed, abrupt change in the arrival flow rate and increase in the percentage of heavy vehicles have led to extreme queue lengths and delay. It has been found that using daily traffic data for scheduling the maintenance and rehabilitation projects could diminish the queue length and delay. Also, by determining the bypass for heavy vehicles, the delay can be significantly reduced; by more than three times. Finally, three models have been presented for estimating the queue length in freeway work zones. Moreover, the procedure shown for creating a queue length model can be used for similar freeways.
|
<li> <b>heavy vehicles:</b> Truck
|
[
[
{
"end": 523,
"label": "vehicleType",
"start": 509
},
{
"end": 769,
"label": "vehicleType",
"start": 755
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 523,
"label": "vehicleType",
"start": 509
},
{
"end": 769,
"label": "vehicleType",
"start": 755
}
] | null | null |
f2062c50-e7ec-4e63-9f0a-35456949177b
|
completed
| 2025-04-09T16:14:38.079713 | 2025-05-26T08:50:39.495983 |
92db07b6-97c0-4b29-98c5-1345dd02c2b0
|
The review of the previous studies on work zones reveals that the delay has been investigated more than any other parameter and the focus has been on the costs of the users.Shibuya et al. [6] showed in a study that the delay resulting from the rate of change of velocity includes 35 to 40 percent of the total delay in the work zones.Nam and Drew [7] presented a numerical model of the queue.The drawback of this model is that it predicts the delay less than the actual amount.This model has some shortcomings in comparison with the kinematic model.Son [8] presented a model for estimating the queue length.The results showed that the delay due to queueing was less than the delay due to lower speed.Migtez et al. [9] investigated the speed limitation in the work zones.They revealed that speed reduction to a maximum of 16 km/hour, reduces accidents.Ullman and Dudek [10] suggested a theory method for estimating the queue length.This method predicts the queue length and delays less than the actual amount according to the data obtained from the field information.They suggested a macroscopic model based on speed, density, and flow rate.Renata et al. [11] studied 17-speed reduction patterns in the work zone and showed that cars and heavy vehicles follow similar patterns.Weng and Meng [12] provided a model for the estimation of capacity in freeway work zone using field information of 18 US work zones.This model has the ability to estimate the total capacity after the speed reduction and queuing.The capacity of the work zone is
|
<li> <b>cars:</b> Car<li> <b>heavy vehicles:</b> Truck
|
[
[
{
"end": 1232,
"label": "vehicleType",
"start": 1228
},
{
"end": 1251,
"label": "vehicleType",
"start": 1237
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 1232,
"label": "vehicleType",
"start": 1228
},
{
"end": 1251,
"label": "vehicleType",
"start": 1237
}
] | null | null |
165271f4-bb26-48c2-9490-299c39b643f0
|
completed
| 2025-04-09T16:14:38.079720 | 2025-05-26T14:21:01.194470 |
c4d18be4-27a7-4550-aaa1-aaefb11b0b8c
|
Highly automated shuttle vehicles (SAE Level 4) have the potential to enhance public transport services by decreasing the demand for drivers, enabling more frequent and flexible ride options. However, at least in a transitionary phase, safety operators that supervise and support the shuttles with their driving tasks may be required on board the vehicle from a technical or legal point of view. A crucial component for executing supervisory and intervening tasks is the human–machine interface between an automated vehicle and its on-board operator. This research presents in-depth case studies from three heterogenous living laboratories in Germany that deployed highly automated shuttle vehicles with on-board operators on public roads. The living labs differed significantly regarding the on-board operators’ tasks and the design of the human–machine interfaces. Originally considered a provisional solution until the vehicle automation is fully capable of running without human support, these interfaces were, in general, not designed in a user-centered way. However, since technological progress has been slower than expected, on-board operator interfaces are likely to persist in the mid-term at least. Hence, this research aims to assess the aptitude of interfaces that are in practical use for the on-board operators’ tasks, in order to determine the user-centered design of future interfaces. Completing questionnaires and undergoing comprehensive, semi-structured interviews, nine on-board operators evaluated their human–machine interfaces in light of the respective tasks they complete regarding user variables such as work context, acceptance, system transparency, and trust. The results were highly diverse across laboratories and underlined that the concrete system setup, encompassing task and interface design, has a considerable impact on these variables. Ergonomics, physical demand, and system transparency were identified as the most significant deficits. These findings and derived recommendations may inform the design of on-board operator workspaces, and bear implications for remote operation workstations as well.
|
<li> <b>Highly automated shuttle vehicles (SAE Level 4):</b> Other Vehicle<li> <b>SAE Level 4:</b> Level 4<li> <b>shuttles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>automated vehicle:</b> Other Vehicle
|
[
[
{
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"label": "levelOfAutomation",
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},
{
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},
{
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"label": "vehicleType",
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},
{
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},
{
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{
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},
{
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"automated shuttle vehicles\" is a \"vehicleType\" (\"shuttle\" is also added as one specific term)"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
"start": 0
},
{
"end": 46,
"label": "levelOfAutomation",
"start": 35
},
{
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"start": 284
},
{
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"label": "vehicleType",
"start": 347
},
{
"end": 523,
"label": "vehicleType",
"start": 516
},
{
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"label": "vehicleType",
"start": 922
},
{
"end": 523,
"label": "vehicleType",
"start": 506
}
] | null | null |
5a8f5ae6-b336-430d-a609-f8c5db2e91b3
|
completed
| 2025-04-09T16:14:38.079727 | 2025-05-13T06:05:27.901112 |
7b49f15d-de2e-4baf-aab3-80533eff00eb
|
The participants were active on-board operators of highly automated shuttle buses in living labs.They were recruited through inquiries made to project partners of the German Aerospace Center (DLR) who are involved in living labs with highly automated shuttle buses, as well as other living labs in Northern Germany.The living labs were HEAT (Hamburg Electric Autonomous Transportation [23]), RealLabHH (Reallabor Hamburg, Real-World Laboratory Hamburg [24]), and TaBuLa (Testzentrum für automatisiert verkehrende Busse im Kreis Herzogtum Lauenburg, Test Center for Automated Buses in the Duchy of Lauenburg [25]), and were all located in and around Hamburg, Germany (further information in Section 2.3).The interviews were conducted in October 2021 and were carried out in person at the operating sites of the respective living labs.The shuttle types from these living labs are still widely used in research projects across Germany, underlining the persisting relevance of the analysis [26].Nine on-board operators, three from each of the three different living labs, took part in the interview study.In the subsequent analysis, they are referred to as P1, P2, and P3 for each living lab.The participants' ages ranged from 24 to 53, with an average of M = 41 years (SD = 10.77years).The duration of employment as an on-board operator ranged from 6 months to 40 months, with an average duration of M = 14.56 months (SD = 12.16 months).Table 1 presents work experience as on-board operators and age per living lab.The participants took part in this study voluntarily and the interviews were conducted during their work hours.This study was conceptualized and conducted in accordance with the Declaration of Helsinki.Informed consent was obtained from all participants before the interview.The participants were allowed to interrupt or end the interview at any point without justification or consequence.
|
<li> <b>shuttle buses:</b> Bus<li> <b>automated shuttle buses:</b> Bus
|
[
[
{
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"label": "vehicleType",
"start": 58
},
{
"end": 264,
"label": "vehicleType",
"start": 241
},
{
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"label": "vehicleType",
"start": 837
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"One more plain \"shuttle\" term was not identified and hereby assigned the vehicleType category"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 81,
"label": "vehicleType",
"start": 68
},
{
"end": 264,
"label": "vehicleType",
"start": 251
},
{
"end": 81,
"label": "vehicleType",
"start": 58
},
{
"end": 264,
"label": "vehicleType",
"start": 241
}
] | null | null |
86c73d32-b97b-4c7f-b312-68ee7ddd57b7
|
completed
| 2025-04-09T16:14:38.079733 | 2025-05-26T14:17:35.343329 |
ac8c1c48-607a-40e7-b1dd-720fbf048221
|
Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.
|
<li> <b>Transportation vehicles:</b> Other Vehicle<li> <b>Electric Vehicles (EVs):</b> Car<li> <b>EV:</b> Car<li> <b>EVs:</b> Car
|
[
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}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"EVs\" could be other vehicles than cars"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
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{
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},
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},
{
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},
{
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"label": "vehicleType",
"start": 118
},
{
"end": 445,
"label": "vehicleType",
"start": 442
}
] | null | null |
f3cf319f-6d6a-41af-83d4-fd6f519ed444
|
completed
| 2025-04-09T16:14:38.079739 | 2025-05-26T14:14:32.565870 |
e89bda09-6070-4005-957d-c83ebf7dabe3
|
This article presents an algorithm that allows one incorporate measured EV occupancy data for modeling EV charging power consumption.The proposed algorithm keeps track of successful charging events and allows one to compare various scenarios of charger configuration, e.g., number of chargers and number daily events.The main focus is on modeling the aggregated daily charger load profiles and comparing them with the load profiles during a demand response event when the maximum available charging power is reduced. Modeling demand response (DR) allows one to peek into future scenarios of an abundance of EVs, to study the available flexibility of EV fleets, and to understand the necessity of installation of additional chargers and the building technical restraints in terms of the peak load across charging infrastructure.One of the main obstacles in the modeling of future scenarios is the uncertainty in EV owner behavioral patterns, e.g., how large of a group of EV owners permits their maximum charging power to diminish during charging in a public domain, such as in the office building.Another large obstacle is unavailability of EV vehicle labeling in the dataset, which constitutes the inability to model spatial coefficients, so that it is possible to take into consideration the geographical location of the charging infrastructure.With a large level of EV penetration, there is a corresponding growing trend in expanding the charging infrastructure, which would result in a more sophisticated relationship between the unavailable charger in the respective building and the possibility of an EV owner to charge the car at the neighboring building.Reference [34] showed that not only the utilization rate of the charging infrastructure depends on their geographical location but also the idle times.Idle time is the period when the EV is plugged in but not charging.The study concluded that utilization of charging stations at the residential level was the highest, followed by the utilization in the office buildings.Additionally, these locations showed the highest idle time.Reference [34] said that there was no specific penalty for the occupation of the charging station without charging.Our original dataset lacks idle time information, which would help to improve the understanding of how time-based payment system would affect the idle times. We illustrated how the proposed algorithm can be utilized to understand if additional charger infrastructure is necessary, depending on the desired average percentage of successful events compared to the daily number of events.Modeling of DR events, e.g., adjusting the maximum allowed power consumption in a time interval, demonstrates that EV flow in coup with the existing charging capacity are the two main factors that affect the existing flexibility potential.Specifically, a larger charging infrastructure potentially provides the largest potential for power adjustment, however, requires an adequate flow of vehicles throughout the studied time interval.At the same time, a large number of daily events that restricts the maximum charging power decreases the number of successful chargings; thus, this ratio is a subjective preference of a building owner or a technical designer.Additional understanding of incoming EV flows is required to study the probabilities of EVs to be in place for DR events, so that aggregators can utilize that information during modeling process, for example, for the frequency containment markets.Perhaps one of the greatest obstacle is scarceness of available data and limited possibility to identify single EV units to create spatial models.
|
<li> <b>EV:</b> Car<li> <b>EVs:</b> Car<li> <b>vehicles:</b> Other Vehicle<li> <b>car:</b> Car
|
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] | null | null |
ea0b4648-5b3c-436e-9336-5d396532baab
|
completed
| 2025-04-09T16:14:38.079746 | 2025-05-13T05:44:00.872643 |
5db6f7a9-d157-4aa4-8bd2-df235ee899e7
|
Many Autonomous Underwater Vehicles (AUVs) need to cope with hazardous underwater medium using a limited computational capacity while facing unknown kinematics and disturbances. However, most algorithms proposed for navigation in such conditions fail to fulfil all conditions at the same time. In this work, we propose an optimal control method, based on a receding horizon approach, namely MPC (Model Predictive Control). Our model also estimates the kinematics of the medium and its disturbances, using efficient tools that rely on the use of linear algebra and first-order optimization methods. We also test our ideas using an extensive set of simulations, which show that the proposed ideas are very competitive in terms of cost and computational efficiency in cases of total and partial observability.
|
<li> <b>Autonomous Underwater Vehicles (AUVs):</b> Other Vehicle
|
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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"submitted"
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[
"Correct"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Agree to link to \"Other Vehicle\""
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
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"label": "vehicleType",
"start": 5
}
] | null | null |
afcdec4b-8cf5-4a71-9a73-10ed58796c40
|
completed
| 2025-04-09T16:14:38.079752 | 2025-05-26T14:04:36.873051 |
6ae86cfa-9fe8-44c9-a5f2-0c20f63073d4
|
The final element in our environment block is the observation model, as the information captured by the AUV sensors need not be the actual next state of the system s n+1 , due to the sensors imperfections.For instance, when the AUV is underwater, it cannot use GPS (Global Positioning System) as a location method due to the losses of the radio signal due to the water.Thus, the AUV must resort to other set of techniques to estimate its location, such as inertial based methods, acoustic sensors or beacon methods, among others [3,4].Hence, this means that in general, the information gathered by the AUV sensors will be an observation o n , a noisy and/or incomplete version of the actual state of the system.Mathematically, we can say that there exists an observation model g such that: where g is a mapping from the state space to the observation space.Depending on the sensors in the AUV and the location method chosen, the observation model g will vary, where we note that each location method brings a certain tradeoff in computational load and precision in the estimation of the state, among others [4].
|
<li> <b>AUV sensors:</b> Other Sensor<li> <b>AUV:</b> Other Vehicle<li> <b>GPS (Global Positioning System):</b> GPS<li> <b>inertial based methods:</b> IMU<li> <b>acoustic sensors:</b> Other Sensor
|
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[
"submitted"
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[
"Partially correct"
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[
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[
"submitted"
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[
"Correct"
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[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
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[
"submitted"
] |
[
"\"AUVs\" and \"acoustic sensors\" not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
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] | null | null |
4bdac574-1ece-4f9e-ba01-94054d01a0fc
|
completed
| 2025-04-09T16:14:38.079758 | 2025-05-26T07:52:40.437400 |
11eab755-7d29-46c2-942b-aa48d4c2eee5
|
This study explores the potential impact of per capita gross domestic product (GDP) changes on the adoption of autonomous vehicles (AVs). The level of adoption of AVs is anticipated to influence the benefits of future mobility, prompting numerous studies that forecast the market share of AVs using various methods. The influence of changes in the per capita GDP on vehicle ownership is crucial in assessing the challenges associated with reducing dependence on AVs in the future. This phenomenon, known as the hysteresis effect, implies that AV adoption estimates may differ when the GDP is rising as opposed to when it is falling. This research examines the effect of rising and falling GDP per capita on the anticipated AV diffusion in Hungary, utilising a scenario-based method to account for the variation in adoption rates in the literature. The study findings indicate that declines in GDP in the past will impact AV ownership, leading to a shift in future adoption patterns. The AV market is projected to reach saturation in the 2070s and the 2090s in favourable and moderate scenarios, respectively, while a pessimistic state would delay this outcome until after the year 2100.
|
<li> <b>autonomous vehicles (AVs):</b> Other Vehicle<li> <b>AVs:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle
|
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e630c08c-a98d-4a78-a595-8ac6f5994f91
|
completed
| 2025-04-09T16:14:38.079764 | 2025-05-20T11:11:07.105052 |
da190888-f65d-4987-8a82-26a9fbffe040
|
Optimists claim that, because about 90% of crashes involve a human error, autonomous vehicles will reduce crash rates by 90% (Kok, et al. 2017;McKinsey 2016), but this overlooks additional risks these technologies can introduce (Hsu 2017;ITF 2018;Kockelman, et al. 2016;Koopman and Wagner 2017;Ohnsman 2014). • Hardware and software failures.Complex electronic systems often fail due to false sensors, distorted signals and software errors.Self-driving vehicles will certainly have failures that contribute to crashes, although their frequency is difficult to predict (Dawn Project 2022). • Malicious hacking.Self-driving technologies can be manipulated for amusement or crime. • Increased risk-taking.When travelers feel safer they tend to take additional risks, called offsetting behavior or risk compensation.For example, autonomous vehicle passengers may reduce seatbelt use, and other road users may be less cautious (Millard-Ball 2016), described as "over-trusting" technology (Ackerman 2017). • Platooning risks.Many potential benefits, such as reduced congestion and pollution emissions, require platooning (vehicles operating close together at high speeds on dedicated lanes), which can introduce new risks, such as human drivers joining platoons and increased crashes severity. • Increased total vehicle travel.By improving convenience and comfort autonomous vehicles may increase total vehicle travel and therefore crash exposure (Trommer, et al. 2016;WSJ 2017). • Additional risks to non-auto travelers.Autonomous vehicles may have difficulty detecting and accommodating pedestrians, bicyclists and motorcycles (PBIC 2017). • Reduced investment in conventional safety strategies.The prospect of autonomous vehicles may reduce future efforts to improve driver safety (Lawson 2018). • Higher vehicle repair costs due to additional equipment.Additional sensors and control systems, and increased quality control, are likely to significantly increase collision repair costs (AAA 2018). Because of these new risks, autonomous vehicles are unlikely to achieve the 90% net crash reductions advocates predict.After analyzing traffic crash risk factors, Mueller, Cicchino, and Zuby (2020) concluded that autonomous vehicles could prevent approximately 34% of crashes.Sivak and Schoettle (2015a) conclude that autonomous vehicles will have crash rates similar to an average driver and may increase total crashes when autonomous and human-driven vehicles mix.Autonomous vehicles currently have high operational failure rates.One study found that Tesla's Full Self Driving vehicles would fail a normal driving test because it averages one maneuver error every three minutes and one critical error every ten minutes (Dawn Project 2022).Tesla vehicles using Autopilot and "Full Self-Driving" programs have experienced numerous crashes and fatalities, which experts consider a higher rate than human-powered vehicles (Siddiqui and Merrill 2023).In 2019, the best autonomous test vehicles experienced one disengagement (human drivers overrode the automated system) per 16,666 miles, but most were more frequent (Hyatt 2020).These examples indicate that autonomous vehicle operating technologies are not ready for broad implementation.City transportation officials concluded that autonomous taxis are currently unsafe (Truong 2023). Groves and Kalra (2017) argue that autonomous vehicle deployment is justified even if they reduce crash rates just 10%, but their analysis does not account for increases in total vehicle travel, for example, if they reduce per-mile crash rates 10% but increase vehicle travel 20%, increasing total crashes and risk to other road users.Shared autonomous vehicles may reduce crashes by providing more affordable alternatives to higher-risk drivers.Efforts to reduce higher-risk driving, such as graduated driver's licenses, special testing for senior drivers, and anti-impaired driver campaigns, can be more effective and publicly acceptable if affected groups have convenient and affordable mobility options.For example, parents may purchase autonomous vehicles for their teenagers, and travelers may use autonomous vehicles after drinking alcohol or taking drugs. Autonomous vehicles are vulnerable to hacking.In one experiment, researchers demonstrated that adding graffiti-like marks to a roadside stop-sign caused software to read an inaccurate "Speed Limit 45" (Eykholt, et al. 2018).There will be an on-going arms race between hackers and software designers over autonomous vehicles control, which will add costs and risks. Many factors will affect these impacts, including how vehicles are programmed, and how they affect total vehicle travel.For example, to increase travel speeds autonomous vehicles can be programmed to take more risks and shortcuts through neighborhoods, to minimize traffic problems they can be programmed to drive slower and avoid congested roads and neighborhood streets.For example, Tesla's self-driving software allows drivers to choose a faster, "assertive" operating mode which frequently violates traffic laws (Wilson 2022a).
|
<li> <b>autonomous vehicles:</b> Other Vehicle<li> <b>sensors:</b> Other Sensor<li> <b>Self-driving vehicles:</b> Other Vehicle<li> <b>autonomous vehicle:</b> Other Vehicle<li> <b>other road users:</b> Other VRU<li> <b>Platooning:</b> Platooning<li> <b>vehicles:</b> Other Vehicle<li> <b>vehicle:</b> Other Vehicle<li> <b>pedestrians:</b> Pedestrian<li> <b>bicyclists:</b> Cyclist<li> <b>motorcycles:</b> Motorcycle<li> <b>Tesla vehicles:</b> Car<li> <b>Autopilot:</b> Other Level of Automation<li> <b>Full Self-Driving:</b> Other Level of Automation<li> <b>automated system:</b> Other Level of Automation<li> <b>autonomous taxis:</b> Other Vehicle<li> <b>Shared autonomous vehicles:</b> Other Vehicle<li> <b>software:</b> Other Level of Automation
|
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},
{
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},
{
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},
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},
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},
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},
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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"label": "vehicleType",
"start": 2741
},
{
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"label": "vehicleType",
"start": 2905
},
{
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"label": "vehicleType",
"start": 2976
},
{
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"label": "vehicleType",
"start": 3681
},
{
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"label": "vehicleType",
"start": 4080
},
{
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"label": "vehicleType",
"start": 4143
},
{
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},
{
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},
{
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"label": "vehicleType",
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},
{
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},
{
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},
{
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"label": "vehicleType",
"start": 1306
},
{
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"label": "vehicleType",
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},
{
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"label": "vehicleType",
"start": 1802
},
{
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"label": "vehicleType",
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},
{
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},
{
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"label": "vehicleType",
"start": 3507
},
{
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"label": "vehicleType",
"start": 3589
},
{
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"label": "vehicleType",
"start": 4662
},
{
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"label": "VRUType",
"start": 1583
},
{
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"label": "VRUType",
"start": 1596
},
{
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"label": "vehicleType",
"start": 1611
},
{
"end": 2749,
"label": "vehicleType",
"start": 2735
},
{
"end": 2765,
"label": "levelOfAutomation",
"start": 2756
},
{
"end": 2788,
"label": "levelOfAutomation",
"start": 2771
},
{
"end": 3059,
"label": "levelOfAutomation",
"start": 3043
},
{
"end": 3291,
"label": "vehicleType",
"start": 3275
},
{
"end": 3689,
"label": "vehicleType",
"start": 3663
},
{
"end": 332,
"label": "levelOfAutomation",
"start": 324
},
{
"end": 432,
"label": "levelOfAutomation",
"start": 424
},
{
"end": 4353,
"label": "levelOfAutomation",
"start": 4345
},
{
"end": 4480,
"label": "levelOfAutomation",
"start": 4472
},
{
"end": 4971,
"label": "levelOfAutomation",
"start": 4963
}
] | null | null |
c325c3bf-61ed-4b03-8743-d348bc74e9bd
|
completed
| 2025-04-09T16:14:38.079770 | 2025-05-26T07:45:52.769532 |
95befdaa-0d92-41a5-a110-a0c67fc70fbc
|
With the increasing number of distributed power sources such as photovoltaic power and wind power and electric vehicles connected to the grid, the structure and operation state of the traditional distribution network have undergone great changes. Therefore, through the establishment of a distributed power grid-connected evaluation system, it has become an important research topic to evaluate the access of distributed power and the carrying capacity of electric vehicles in the distribution network of new power systems. Firstly, the situation of distributed power supply and electric vehicles and the impact of grid connection are introduced. Secondly, the traditional distribution network carrying capacity evaluation method is studied and introduced. Then, the distribution network carrying capacity evaluation considering uncertainty is reviewed and investigated. Finally, in view of the lack of existing research, further research is needed, aiming to provide a reference for the realization of distributed power supply and grid-connected EV carrying capacity evaluation and a scientific basis for the future operation planning of distributed power supply and EV integration into the distribution network.
|
<li> <b>electric vehicles:</b> Car<li> <b>EV:</b> Car
|
[
[
{
"end": 119,
"label": "vehicleType",
"start": 102
},
{
"end": 473,
"label": "vehicleType",
"start": 456
},
{
"end": 596,
"label": "vehicleType",
"start": 579
},
{
"end": 1049,
"label": "vehicleType",
"start": 1047
},
{
"end": 1170,
"label": "vehicleType",
"start": 1168
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"electric vehicles / EVs\" could be 2-wheelers, 3-wheelers or even mini vans"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 119,
"label": "vehicleType",
"start": 102
},
{
"end": 473,
"label": "vehicleType",
"start": 456
},
{
"end": 596,
"label": "vehicleType",
"start": 579
},
{
"end": 1049,
"label": "vehicleType",
"start": 1047
},
{
"end": 1170,
"label": "vehicleType",
"start": 1168
}
] | null | null |
f0c9abda-bfc3-4c9c-b81c-34bc23ed1997
|
completed
| 2025-04-09T16:14:38.079776 | 2025-05-26T08:36:06.741075 |
d97d6bac-aad6-4f7e-b88c-cfbb5e83b5ab
|
After the distributed power supply is connected to the grid, it can not only reduce the transmission power of the transmission line in the distribution network but also transmit reactive power to make the node voltage more stable [6].The grid connection of distributed power increases the short-circuit capacity of the grid connection point, reduces the probability of system failure, and makes the distribution network more intelligent.The same optimal control of the distributed power supply and power quality device not only changes the power quality of the distribution network but also reduces the cost of the comprehensive power quality management of distribution network equipment.However, the grid connection of distributed power also brings problems to the distribution network that cannot be ignored.The traditional distribution network usually presents a one-way radial power supply mode, and then multiple distributed power sources are connected to the grid, causing voltage sag and other problems, which changes the power flow distribution.At the same time, the distributed power supply contains a large number of nonlinear power electronic switching devices, resulting in an increase in harmonic content in the distribution network, which can seriously lead to the occurrence of harmonic resonance, which is not conducive to the safe operation of the distribution network [10].Figure 4 shows the simulation of the fast Fourier transform analysis of the harmonic after the grid-connected distributed power supply [11].Due to the randomness and uncertainty of the grid connection of the distributed power supply, the uncoordinated operation of the distributed power supply and the load result in a great change in the line power flow, resulting in voltage flicker and fluctuation.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
3ad41667-2d45-4b4e-b062-5cb2c9d75bd4
|
completed
| 2025-04-09T16:14:38.079782 | 2025-05-26T07:14:05.247136 |
fc45701b-b97d-4e5e-8a8f-f20f4c176f25
|
Electric vehicle battery second-life applications are gaining attention as a way to minimize the environmental impact and increase economic profits. However, the demand for stationary energy storage is expected to be saturated in the near future with these second-life batteries. This fact, in addition to the several technical and economic challenges of second-life batteries, promotes exploring other alternatives. This work analyses and compares these possible approaches in terms of battery degradation and economic profit. The results show that for large batteries, intensive Vehicle to Grid does not cause an early retirement of the battery and allows reducing the underuse of the battery. In addition, for the same battery size, Vehicle to Grid provides more economic profit than second-life applications. Nevertheless, only in a few cases does this appear to be more profitable than simply utilizing the battery for driving. Importantly, this study has shown how the assessment of the second-life tends to be too optimistic as a consequence of assuming a fixed End of Life threshold for the batteries.
|
<li> <b>Electric vehicle:</b> Car<li> <b>Vehicle to Grid:</b> V2N
|
[
[
{
"end": 16,
"label": "vehicleType",
"start": 0
},
{
"end": 596,
"label": "entityConnectionType",
"start": 581
},
{
"end": 751,
"label": "entityConnectionType",
"start": 736
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"electric vehicle\" may be a two- or three- wheeler"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 16,
"label": "vehicleType",
"start": 0
},
{
"end": 596,
"label": "entityConnectionType",
"start": 581
},
{
"end": 751,
"label": "entityConnectionType",
"start": 736
}
] | null | null |
37811cf7-76ef-42e9-8277-8844dd1cb110
|
completed
| 2025-04-09T16:14:38.079788 | 2025-05-13T06:10:47.418353 |
0b255a20-224d-4c7e-a0fc-4e56826c2aa7
|
Figure 3a shows the EoL battery capacity and SoH for different sizes considering the fixed threshold.All batteries, except those having 16 kWh, reach the EoL while being able to cover the required range.However, the mileage performed by the EVs is, in all cases, lower than the assumed mileage of the EoL vehicles, especially for the small-capacity batteries.Figure 3b shows a more realistic scenario where the functional EoL is considered, meaning that the battery and EV reached the EoL at the same time, with the previously derived mileage of 344,532 km after 20 years. Considering the 20-year lifespan, batteries with 16 and 24 kWh of capacity are not able to reach the EoL alongside the EV while providing the required driving range.The 30 kWh batteries, on the other hand, hold enough capacity but reach the EoL with an important level of degradation (55.6% SoH), with potential safety and underperformance issues linked to this state.Batteries of 40 kWh and above exceed the required capacity while maintaining a relatively healthy state, over 60% SoH, indicating that a capacity of 40 kWh would be enough to meet the requirements of most drivers (1-No Grid Services). Larger batteries are able to reach the EoL with a large residual capacity and with SoH values over 70%.Therefore, these batteries could either extend their use during their first life by providing V2G services (2-V2G) or serve a second-life application after the EoL (3 -Second-life).
|
<li> <b>EVs:</b> Car<li> <b>EV:</b> Car<li> <b>V2G:</b> V2N
|
[
[
{
"end": 244,
"label": "vehicleType",
"start": 241
},
{
"end": 472,
"label": "vehicleType",
"start": 470
},
{
"end": 694,
"label": "vehicleType",
"start": 692
},
{
"end": 1376,
"label": "entityConnectionType",
"start": 1373
},
{
"end": 1392,
"label": "entityConnectionType",
"start": 1389
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 244,
"label": "vehicleType",
"start": 241
},
{
"end": 472,
"label": "vehicleType",
"start": 470
},
{
"end": 694,
"label": "vehicleType",
"start": 692
},
{
"end": 1376,
"label": "entityConnectionType",
"start": 1373
},
{
"end": 1392,
"label": "entityConnectionType",
"start": 1389
}
] | null | null |
44fe7447-41ce-4f6c-99b2-9bacddc6fa5a
|
completed
| 2025-04-09T16:14:38.079795 | 2025-05-13T06:11:48.945638 |
19d6f9ca-90ec-4be6-9977-b91fcfcbf195
|
Charging of plug-in electric vehicles (PEVs) exposes smart grid systems and their users to different kinds of security and privacy attacks. Hence, a secure charging protocol is required for PEV charging. Existing PEV charging protocols are usually based on insufficiently represented and simplified charging models that do not consider the user’s charging modes (charging at a private location, charging as a guest user, roaming within one’s own supplier network or roaming within other suppliers’ networks). However, the requirement for charging protocols depends greatly on the user’s charging mode. Consequently, available solutions do not provide complete protocol specifications. Moreover, existing protocols do not support anonymous user authentication and payment simultaneously. In this paper, we propose a comprehensive end-to-end charging protocol that addresses the security and privacy issues in PEV charging. The proposed protocol uses nested signatures to protect users’ privacy from external suppliers, their own suppliers and third parties. Our approach supports anonymous user authentication, anonymous payment, as well as anonymous message exchange between suppliers within a hierarchical smart grid architecture. We have verified our protocol using the AVISPA software verification tool and the results showed that our protocol is secure and works as desired.
|
<li> <b>plug-in electric vehicles (PEVs):</b> Car<li> <b>PEV:</b> Car<li> <b>PEVs:</b> Car
|
[
[
{
"end": 44,
"label": "vehicleType",
"start": 12
},
{
"end": 193,
"label": "vehicleType",
"start": 190
},
{
"end": 216,
"label": "vehicleType",
"start": 213
},
{
"end": 911,
"label": "vehicleType",
"start": 908
},
{
"end": 43,
"label": "vehicleType",
"start": 39
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 44,
"label": "vehicleType",
"start": 12
},
{
"end": 193,
"label": "vehicleType",
"start": 190
},
{
"end": 216,
"label": "vehicleType",
"start": 213
},
{
"end": 911,
"label": "vehicleType",
"start": 908
},
{
"end": 43,
"label": "vehicleType",
"start": 39
}
] | null | null |
4ecacc12-13d1-4997-8b02-0a79282ac973
|
completed
| 2025-04-09T16:14:38.079801 | 2025-05-13T06:40:18.997726 |
d3850cb3-7065-4728-8df5-3a993fe07d0a
|
By applying nested signatures, our method supports secure and privacy-aware charging within a hierarchal smart grid architecture which may include primary and secondary suppliers. Aside from this, similar to the approach taken in [17], our method supports user-based authentication to avoid misuse of electric vehicles and implement fair payment. In summary, our original contributions in this our work include: identifying the different charging modes and devising charging models (architectures) accordingly, a comprehensive end to end charging protocol and method that covers all charging modes (private charging, guest user charging, IRC and ERC), anonymous authentication and payment by the user and anonymous message exchange between suppliers for user authentication and payment transactions.Our work relies on the use of cryptographic hash functions and cryptography systems, both symmetric and asymmetric, to ensure confidentiality, integrity and to some extent anonymity.Symmetric key cryptography techniques utilize symmetric secret keys between any two communication entities where asymmetric (also known as a public key) cryptography systems utilize the use of public/private key pairs for secure communication and exchange of information.For more information on cryptography and hash functions, the reader is referred to [25]. The rest of this paper is organized as follows: Section 2 reviews related works.Section 3 introduces the various charging modes and their architectures.The proposed protocol for secure charging and payment transaction is discussed in Section 4. We present the formal verification of the proposed protocol in Section 5. Section 6 concludes the paper.
|
<li> <b>electric vehicles:</b> Car
|
[
[
{
"end": 318,
"label": "vehicleType",
"start": 301
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 318,
"label": "vehicleType",
"start": 301
}
] | null | null |
e7e34e08-b3b3-4f5f-8e09-a6a1a3f8767b
|
completed
| 2025-04-09T16:14:38.079807 | 2025-05-26T07:13:13.197657 |
19eb49ab-eb74-486e-9a73-92c06d809b94
|
Autonomous underwater vehicles (AUVs) are an important equipment for ocean investigation. Actuator fault diagnosis is essential to ensure the sailing safety of AUVs. However, the lack of failure data for training due to unknown ocean environments and unpredictable failure occurrences is challenging for fault diagnosis. In this paper, a meta-self-attention multi-scale convolution neural network (MSAMS–CNN) is proposed for the actuator fault diagnosis of AUVs. Specifically, a two-dimensional spectrogram of the vibration signals obtained by a vibration sensor is used as the neural network’s inputs. The diagnostic model is fitted by executing a subtask-based gradient optimization procedure to generate more general degradation knowledge. A self-attentive multi-scale feature extraction approach is used to utilize both global and local features for learning important parameters autonomously. In addition, a meta-learning method is utilized to train the diagnostic model without a large amount of labeled data, which enhances the generalization ability and allows for cross-task training. Experimental studies with real AUV data collected by vibration sensors are conducted to validate the effectiveness of the MSAMS–CNN. The results show that the proposed method can diagnose the rudder and thruster faults of AUVs in the cases of few-shot diagnosis.
|
<li> <b>Autonomous underwater vehicles (AUVs):</b> Other Vehicle<li> <b>AUVs:</b> Other Vehicle<li> <b>vibration sensor:</b> Other Sensor
|
[
[
{
"end": 562,
"label": "sensorType",
"start": 546
}
]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Partially correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"\"autonomous underwater vehicles (AUVs)\" and \"AUV\" are not of interest to CCAM"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
{
"end": 37,
"label": "vehicleType",
"start": 0
},
{
"end": 36,
"label": "vehicleType",
"start": 32
},
{
"end": 164,
"label": "vehicleType",
"start": 160
},
{
"end": 461,
"label": "vehicleType",
"start": 457
},
{
"end": 1320,
"label": "vehicleType",
"start": 1316
},
{
"end": 562,
"label": "sensorType",
"start": 546
}
] | null | null |
9089a45f-17b1-4a1c-9d05-aff447e9081f
|
completed
| 2025-04-09T16:14:38.079813 | 2025-05-26T08:46:54.602769 |
b1515846-b33d-4441-a1cb-06889218e649
|
To demonstrate the rapid convergence property of the proposed method, we compared it with the transfer network, RNN, and LSTM architectures set up in Section 4.2.All variables, except for the network model, were kept consistent and convergence time was utilized as the performance metric for comparison.We conducted the experiments on a computer with the same hardware configuration and used the thruster data as the source domain data and the rudder data as the target domain data.The specific results are shown in Figure 19. Based on the experimental results, it is evident that the proposed method requires the shortest amount of time for each training round, while the LSTM model takes the longest time.This can be attributed to the fact that the proposed method is a shallow model with fewer training parameters, which allows it to learn the optimal parameters more rapidly.Moreover, as highlighted in Section 4.3, the proposed method exhibits fast convergence and superior recognition performance.
|
None
|
[
[]
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
"Correct"
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[
null
] |
[
"4b8c3fe0-5d19-47b2-b4b5-276e7f0356d0"
] |
[
"submitted"
] |
[] | null | null |
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