| title | SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing |
|---|---|
| authors | Zhouheng Li, Fangguo Zhao, Mattia Piccinini, Baha Zarrouki, Yuan Gao, Zitong Shan, Johannes Betz, Chen Lv, Lei Xie |
| date | 2026-07-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.25388 |
| affiliation | TUM - Professorship of Autonomous Vehicle Systems |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversity and computational efficiency. To address this challenge, we propose Sampling-based Game-Theoretic Planning (SGTP), a real-time framework that combines game-theoretic reasoning with GPU-accelerated sampling of control sequences and dynamics rollouts. Sampled trajectories are ranked using a game-aware cost to capture competitive interactions and generate diverse racing behaviors. Our planner then performs feasibility selection by explicitly enforcing track-boundary and dynamic collision-avoidance constraints, ensuring safe and reliable transitions between racing strategies. Extensive simulations on challenging tracks show that SGTP achieves a 95.24% win rate and a 99.35% task-completion ratio in highly interactive races, with a mean computational time of 0.095 s over multiple iterative solving steps. We also demonstrate the successful application of SGTP in large-scale scenarios with up to 10 agents. We release our code and provide an open-source benchmark of multi-agent autonomous racing algorithms to facilitate future research. Project page: https://sgtp-racing.github.io/. |
cite
@misc{li2026sgtp,
title={SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing},
author={Zhouheng Li and Fangguo Zhao and Mattia Piccinini and Baha Zarrouki and Yuan Gao and Zitong Shan and Johannes Betz and Chen Lv and Lei Xie},
year={2026},
eprint={2607.25388},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.25388}
}