Improve model card for Fail2Drive and TransFuser++

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by nielsr HF Staff - opened
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  1. README.md +42 -11
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  ---
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- license: cc-by-4.0
 
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  library_name: pytorch
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-
 
 
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  tags:
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  - carla
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  - carla-simulator
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  - carla-leaderboard
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  - transfuser
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  - transfuser++
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-
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- model_name: TransFuser++ (TFv5) CARLA Garage Checkpoints
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- language:
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- - en
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  ---
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  # TransFuser++ (TFv5) CARLA Garage Checkpoints
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- This repository re-uploads the pretrained checkpoints originally released by the CARLA Garage project:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- https://github.com/autonomousvision/carla_garage
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- https://s3.eu-central-1.amazonaws.com/avg-projects-2/garage_2/models/pretrained_models.zip
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- All credit and attribution belong to the original authors.
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- Please refer to the original repository for code, training setup, evaluation details, and citations.
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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  library_name: pytorch
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+ license: cc-by-4.0
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+ model_name: TransFuser++ (TFv5) CARLA Garage Checkpoints
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+ pipeline_tag: robotics
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  tags:
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  - carla
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  - carla-simulator
 
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  - carla-leaderboard
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  - transfuser
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  - transfuser++
 
 
 
 
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  ---
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  # TransFuser++ (TFv5) CARLA Garage Checkpoints
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+ This repository contains pretrained checkpoints for the TransFuser++ (TFv5) model, which serves as a baseline for the **Fail2Drive** benchmark.
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+ [**Fail2Drive: Benchmarking Closed-Loop Driving Generalization**](https://huggingface.co/papers/2604.08535)
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+ [**Project Page**](https://simonger.github.io/fail2drive/) | [**Code**](https://github.com/autonomousvision/fail2drive)
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+
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+ ## Introduction
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+ 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.
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+
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+ ## Sample Usage
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+ To run the TransFuser++ model within the Fail2Drive environment, follow the installation instructions in the [official repository](https://github.com/autonomousvision/fail2drive).
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+ Once the environment and simulator are set up, you can evaluate the agent using the following command:
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+ ```bash
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+ LIVE_VISU=1 python leaderboard/leaderboard/leaderboard_evaluator_local.py \
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+ --routes ${WORK_DIR}/fail2drive_split/Generalization_PedestriansOnRoad_1085.xml \
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+ --agent ${WORK_DIR}/team_code/sensor_agent.py \
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+ --agent-config /path/to/checkpoint_folder
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+ ```
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+
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+ *Note: The `--agent-config` parameter should point to the directory containing both `model.pth` and `config.json`.*
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+
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+ ## Attribution
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+ This repository re-uploads the pretrained checkpoints originally released by the [CARLA Garage](https://github.com/autonomousvision/carla_garage) project. All credit and attribution belong to the original authors.
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+ Original source: [CARLA Garage Models](https://s3.eu-central-1.amazonaws.com/avg-projects-2/garage_2/models/pretrained_models.zip)
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+ ## Citation
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+ ```bibtex
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+ @article{ger2024fail2drive,
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+ title={Fail2Drive: Benchmarking Closed-Loop Driving Generalization},
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+ author={Ger, Simon and others},
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+ journal={arXiv preprint arXiv:2604.08535},
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+ year={2024}
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+ }
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+ ```