Haochen Tian, Tianyu Li, Haochen Liu, Jiazhi Yang, Yihang Qiu, Guang Li, Junli Wang, Yinfeng Gao, Zhang Zhang, Liang Wang, Hangjun Ye, Tieniu Tan, Long Chen, Hongyang Li
📜 [technical report], 🏠 [project page]
📧 Primary Contact: Haochen Tian (tianhaochen2023@ia.ac.cn)
- 🏗️ A scalable simulation pipepline that synthesizes diverse and high-fidelity reactive driving scenarios with pseudo-expert demonstrations.
- 🚀 An effective sim-real co-training strategy that improves robustness and generalization synergistically across various end-to-end planners.
- 🔬 A comprehensive recipe that reveals crucial insights into the underlying scaling properties of sim-real learning systems for end-to-end autonomy.
- [2025/12/1] We released our paper on arXiv.
- Simulation Data release (Dec. 2025).
- Sim-Real Co-training Code release (Dec. 2025).
- Checkpoints release (Dec. 2025).
If any parts of our paper and code help your research, please consider citing us and giving a star to our repository.
@article{tian2025simscale,
title={SimScale: Learning to Drive via Real-World Simulation at Scale},
author={Haochen Tian and Tianyu Li and Haochen Liu and Jiazhi Yang and Yihang Qiu and Guang Li and Junli Wang and Yinfeng Gao and Zhang Zhang and Liang Wang and Hangjun Ye and Tieniu Tan and Long Chen and Hongyang Li},
journal={arXiv preprint arXiv:2511.23369},
year={2025}
}All content in this repository is under the Apache-2.0 license. The released data is based on nuPlan and are under the CC-BY-NC-SA 4.0 license.
We acknowledge all the open-source contributors for the following projects to make this work possible: