Temporal TransFuser++

About

This repo contains the model weights for adapted temporal versions of the TransFuser++ model by Jaeger et. al. 2023.

These models were trained as part of a master's thesis titeled "Temporal Fusion in Imitation-Based End-to-End Autonomous Driving" at the Norwegian University of Science and Technology (NTNU). The thesis will be linked when it becomes available.

All model weights and checkpoint states can be found under "Files and versions".

Results on Bench2Drive

Experiment Overall Multi-ability metrics
DS SR Merge Overtake Emergency Brake Give Way Traffic Sign Mean
static-8x1 81.68 62.27 57.92 44.44 76.67 46.67 78.42 60.82
static-8x2 83.22 64.24 59.68 45.19 81.11 50.00 79.65 63.13
lidar-4x1 63.58 36.52 33.07 20.00 35.00 46.67 67.54 40.46
full-4x1 79.78 56.06 47.08 41.48 71.67 50.00 76.84 57.41
static-4x1 81.25 60.45 50.63 46.67 80.00 50.00 77.19 60.90
notraj-4x1 81.14 60.45 54.62 45.19 77.22 50.00 74.56 60.32
noprune-4x1 82.80 63.79 54.20 49.63 83.79 50.00 80.00 63.52
large-4x1 82.22 63.03 58.33 40.74 80.00 50.00 81.75 62.17
Default TF++ 80.87 62.58 58.33 42.22 74.44 46.67 80.88 60.51

DS = driving score

SR = success rate

Multi-ability metrics are defined by Bench2Drive [ArXiv].

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