SGTP：基于采样的博弈论规划方法，用于实时多车辆自动驾驶赛车

摘要

自主多车竞速要求在激烈交互中实时规划多样化的竞争性行为。现有规划器往往难以兼顾策略多样性与计算效率。针对这一挑战，我们提出基于采样的博弈论规划（SGTP）方法，这是一种将博弈论推理与GPU加速的控制序列采样及动力学展开相结合的实时框架。采样的轨迹通过博弈感知代价函数进行排序，以捕捉竞争性交互并生成多样化的竞速行为。随后，我们的规划器通过显式施加赛道边界约束和动态避碰约束来进行可行性选择，确保竞速策略之间安全可靠的切换。在具有挑战性的赛道上进行的大量仿真实验表明，SGTP在高交互性竞速中实现了95.24%的胜率和99.35%的任务完成率，且多轮迭代求解的平均计算时间为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。我们公开了代码，并提供了多智能体自主竞速算法的开源基准测试，以促进未来研究。项目主页：https://sgtp-racing.github.io/。

English

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/.