language:
- en
library_name: pytorch
license: cc-by-4.0
model_name: TransFuser++ (TFv5) CARLA Garage Checkpoints
pipeline_tag: robotics
tags:
- carla
- carla-simulator
- autonomous-driving
- imitation-learning
- end-to-end-driving
- carla-leaderboard
- transfuser
- transfuser++
TransFuser++ (TFv5) CARLA Garage Checkpoints
This repository contains pretrained checkpoints for the TransFuser++ (TFv5) model, which serves as a baseline for the Fail2Drive benchmark.
Fail2Drive: Benchmarking Closed-Loop Driving Generalization
Introduction
Fail2Drive is a paired-route benchmark for closed-loop generalization in CARLA, featuring 200 routes and 17 new scenario classes. It highlights failure modes in state-of-the-art models when facing distribution shifts in appearance, layout, behavior, and robustness. TransFuser++ is an end-to-end autonomous driving agent that utilizes both LiDAR and camera inputs.
Sample Usage
To run the TransFuser++ model within the Fail2Drive environment, follow the installation instructions in the official repository.
Once the environment and simulator are set up, you can evaluate the agent using the following command:
LIVE_VISU=1 python leaderboard/leaderboard/leaderboard_evaluator_local.py \
--routes ${WORK_DIR}/fail2drive_split/Generalization_PedestriansOnRoad_1085.xml \
--agent ${WORK_DIR}/team_code/sensor_agent.py \
--agent-config /path/to/checkpoint_folder
Note: The --agent-config parameter should point to the directory containing both model.pth and config.json.
Attribution
This repository re-uploads the pretrained checkpoints originally released by the CARLA Garage project. All credit and attribution belong to the original authors.
Original source: CARLA Garage Models
Citation
@article{ger2024fail2drive,
title={Fail2Drive: Benchmarking Closed-Loop Driving Generalization},
author={Ger, Simon and others},
journal={arXiv preprint arXiv:2604.08535},
year={2024}
}