发表机构
Zhejiang University; Nanyang Technological University; Technical University of Munich（浙江大学; 南洋理工大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主多车辆赛车中平衡战略多样性与计算效率的难题，提出基于采样的博弈论规划（SGTP）框架，结合博弈论推理与GPU加速采样，经可行性选择确保安全过渡，模拟显示其在多智能体场景表现良好，还开源代码及基准促进研究。
AI 中文摘要
自主多车辆赛车需要在激烈的交互中实时规划各种竞争行为。现有规划器往往难以平衡战略多样性和计算效率。为应对这一挑战，我们提出了基于采样的博弈论规划（SGTP），这是一个实时框架，将博弈论推理与GPU加速的控制序列采样和动力学展开相结合。使用具有博弈意识的成本对采样轨迹进行排序，以捕捉竞争交互并生成多样的赛车行为。然后，我们的规划器通过明确执行赛道边界和动态碰撞避免约束来进行可行性选择，确保赛车策略之间的安全可靠过渡。在具有挑战性的赛道上进行的广泛模拟表明，SGTP在高度交互式比赛中实现了95.24%的胜率和99.35%的任务完成率，在多个迭代求解步骤中的平均计算时间为0.095秒。我们还展示了SGTP在多达10个智能体的大规模场景中的成功应用。我们发布了代码，并提供了多智能体自主赛车算法的开源基准，以促进未来的研究。项目页面：此https URL。
英文摘要
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/.
