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Table of Contents:

1. News
2. Dataset
3. Benchmark
4. License
5. Citation

News

• [2024/07/10] We further clean and add more clips in the Full set (13638 clips). Since HuggingFace only allows up to 10000 files per repo, we use two repos to store the Full set. As suggested in this issue issue, we add a filelist and sha256 of clips for each set.
• [2024/06/19] Due to a typo in the upload script for HuggingFace, all clips of scenario VehicleTurningRoutePedestrian are empty in the HuggingFace version. We have fixed that. Please check your data to make sure they are not empty.
• [2024/06/05] Bench2Drive realases the Full dataset (10000 clips), evaluation tools, baseline code, and benchmark results.
• [2024/04/27] Bench2Drive releases the Mini (10 clips) and Base (1000 clips) split of the official training data.

Dataset

• The datasets has 3 subsets, namely Mini (10 clips), Base (1000 clips) and Full (10000 clips), to accommodate different levels of computational resource.
• Detailed explanation of dataset structure, annotation information, and visualization of data.

Subset	Hugging Face	Baidu Cloud	Approx. Size	File List
Mini	Download script	-	4G	Mini Json File
Base	Hugging Face Link	Baidu Cloud Link	400G	Base Json File
Full	Full HF Link - 9888 files/Sup HF Link - 3814 file	-	4T	Full/Sup Json File

Note that the Mini Set is 10 representative scenes. You may download them by manually select file names from the Base set.

Use the command line: huggingface-cli download --repo-type dataset --resume-download rethinklab/Bench2Drive --local-dir Bench2Drive-Base to download from hugginface. User may consider mirror site if Huggingface is blocked. Use BaiduPCS-Go to download from Baidu Cloud. Both command lines are resumable.

Baseline Code

• Uniad/VAD in Bench2Drive
• TCP/ADMLP in Bench2Drive

Setup

• Download and setup CARLA 0.9.15

      mkdir carla
      cd carla
      wget https://carla-releases.s3.us-east-005.backblazeb2.com/Linux/CARLA_0.9.15.tar.gz
      tar -xvf CARLA_0.9.15.tar.gz
      cd Import && wget https://carla-releases.s3.us-east-005.backblazeb2.com/Linux/AdditionalMaps_0.9.15.tar.gz
      cd .. && bash ImportAssets.sh
      export CARLA_ROOT=YOUR_CARLA_PATH
      echo "$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.15-py3.7-linux-x86_64.egg" >> YOUR_CONDA_PATH/envs/YOUR_CONDA_ENV_NAME/lib/python3.7/site-packages/carla.pth # python 3.8 also works well, please set YOUR_CONDA_PATH and YOUR_CONDA_ENV_NAME

Eval Tools

• Add your agent to leaderboard/team_code/your_agent.py & Link your model folder under the Bench2Drive directory.

      Bench2Drive\ 
        assets\
        docs\
        leaderboard\
          team_code\
            --> Please add your agent HEAR
        scenario_runner\
        tools\
        --> Please link your model folder HEAR

• Debug Mode

      # Verify the correctness of the team agent， need to set GPU_RANK, TEAM_AGENT, TEAM_CONFIG
      bash leaderboard/scripts/run_evaluation_debug.sh

• Multi-Process Multi-GPU Parallel Eval. If your team_agent saves any image for debugging, it might take lots of disk space.

      # Please set TASK_NUM, GPU_RANK_LIST, TASK_LIST, TEAM_AGENT, TEAM_CONFIG, recommend GPU: Task(1:2).
      # It is normal that certain model can not finsih certain routes, no matter how many times we restart the evaluation. It should be treated as failing as it usually happens in the routes where agents behave badly.
      bash leaderboard/scripts/run_evaluation_multi.sh 

• Visualization - make a video for debugging with canbus info printed on the sequential images.

      python tools/generate_video.py -f your_rgb_folder/

• Metric

      # Merge eval json and get driving score and success rate
      # This script will assume the total number of routes with results is 220. If there is not enough, the missed ones will be treated as 0 score.
      python tools/merge_reoute_json.py -f your_json_folder/
  
      # Get multi-ability results
      python tools/ability_benchmark.py -r merge.json

Benchmark

License

All assets and code are under the Apache 2.0 license unless specified otherwise.

Citation

Please consider citing our papers if the project helps your research with the following BibTex:

@article{jia2024bench,
  title={Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving},
  author={Xiaosong Jia and Zhenjie Yang and Qifeng Li and Zhiyuan Zhang and Junchi Yan},
  journal={arXiv preprint arXiv:2406.03877},
  year={2024}
}

@inproceedings{li2024think,
  title={Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)},
  author={Qifeng Li and Xiaosong Jia and Shaobo Wang and Junchi Yan},
  booktitle={ECCV},
  year={2024}
}