Skip to content

Thinklab-SJTU/Bench2Drive-Jittor

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Bench2DriveZoo - Jittor

Introduction (介绍)

This repo contains the Jittor implementation of Bench2DriveZoo, which supports BEVFormer, UniAD , VAD in Bench2Drive. All models are student models of the world model RL teacher - Think2Drive.

本仓库是Bench2DriveZoo基于计图国产深度学习框架的实现,支持在闭环端到端自动驾驶测试基准Bench2Drive中运行。本项目在JAD基础上,将UniADVAD适配到CARLA仿真,使用基于世界模型的强化学习教师Think2Drive采集的数据进行模仿学习得到。

We also implement AD-MLP and TCP in Bench2Drive under Jittor. Use "git checkout tcp/admlp" to obtain their corresponding training and evaluation code.

我们也实现了Jiitor框架的AD-MLPTCP在CARLA下的版本。请使用"git checkout tcp/admlp"切换到对应的分支。

Prepare (配置环境 & 准备数据集)

Open-loop evaluation (开环验证)

  1. Prepare your checkpoint, or download our pretrained models 准备好自己的checkpoint 或者 使用预训练好的模型。

  2. Open the root directory, run: 在本目录下运行:

bash ./adzoo/uniad(vad)/uniad(vad)_jittor_eval.sh ./adzoo/uniad(vad)/configs/.../your_config.py /path/to/xxx.pth 1

e.g.:
bash ./adzoo/uniad/uniad_jittor_eval.sh ./adzoo/uniad/configs/stage2_e2e/base_e2e_b2d.py ./ckpts/uniad_base_b2d.pth 1
bash ./adzoo/vad/vad_jittor_eval.sh ./adzoo/vad/configs/VAD/VAD_base_e2e_b2d.py ./ckpts/vad_b2d_base.pth 1

Close-loop evaluation (闭环评测)

  • Follow Bench2Drive Official Repo to install CARLA and CARLA python egg.
  • link the two agents uniad_b2d_agent_jittor.py and vad_b2d_agent_jittor under leaderboard/team_code folder of Bench2Drives
  • Modify the script "run_evaluation_debug.sh" to configure the team code agent, model config, and model checkpoint to run.
  • Open the root directory of Bench2Drive, then
bash ./leaderboard/scripts/run_evaluation_debug.sh

Citation (引用)

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

如果您觉得本项目对您有帮助,请考虑引用:

@article{hu2020jittor,
  title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
  author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
  journal={Science China Information Sciences},
  volume={63},
  number={222103},
  pages={1--21},
  year={2020}
}

@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}
}

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages