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Important

🌟 Stay up to date at opendrivelab.com!

ReSim

Reliable World Simulation for Autonomous Driving

Jiazhi Yang, Kashyap Chitta, Shenyuan Gao, Long Chen, Yuqian Shao, Xiaosong Jia, Hongyang Li, Andreas Geiger, Xiangyu Yue, Li Chen

📜 [technical report], 🎬 [video demos]

📧 Primary Contact: Jiazhi Yang (jzyang@link.cuhk.edu.hk)

✍️ Overview

  • ReSim is a driving world model that enables Reliable Simulation of diverse open-world driving scenarios under various actions, including hazardous non-expert ones. A Video2Reward model estimates the reward from ReSim’s simulated future.

  • The key ingredient is to co-train the world model on heterogeneous driving data including driving videos from the web, driving data with action labels, and simulated data with non-expert driving behaviors.

  • The high-fidelity prediction, accurate action-following, and reward estimation abilities of ReSim facilitate multiple driving applications.

📋 TODO List

  • Code release (Estimated in July).
  • Pretrained weights for ReSim world model.
  • Simulated data from CARLA with non-expert behaviors.

⭐ Citation

If any parts of our paper and code help your research, please consider citing us and giving a star to our repository.

@article{yang2025resim,
  title={ReSim: Reliable World Simulation for Autonomous Driving},
  author={Jiazhi Yang and Kashyap Chitta and Shenyuan Gao and Long Chen and Yuqian Shao and Xiaosong Jia and Hongyang Li and Andreas Geiger and Xiangyu Yue and Li Chen},
  journal={arXiv preprint arXiv:2506.09981},
  year={2025}
}