用游戏博弈+采样实现多车竞速实时规划，兼顾策略多样与计算效率。
SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing
- 基于博弈成本对采样轨迹排序，捕捉竞争互动。
- 95.24%胜率，99.35%任务完成率，平均耗时0.095秒。
- 支持最多10车协同，开源代码与评测基准。
多车自主竞速需要在激烈交互中实时规划多样化的竞争行为。现有规划器常难以平衡策略多样性与计算效率。为此，我们提出基于采样的博弈规划框架（SGTP），结合博弈论推理与GPU加速的控制序列及动态演化采样。通过引入博弈感知代价对采样轨迹排序，捕获竞争互动并生成多样化竞速行为。随后通过显式施加赛道边界与动态避撞约束进行可行性筛选，确保策略切换的安全可靠。在复杂赛道上的大量仿真表明，SGTP在高度交互比赛中实现了95.24%的胜率和99.35%的任务完成率，多次迭代求解的平均计算时间为0.095秒。我们还在最多10个智能体的大规模场景中验证了其有效性。项目代码已开源，并提供多智能体自主竞速算法的公开评测基准，以推动后续研究。项目主页：https://sgtp-racing.github.io/。
原文摘要 · Abstract (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/.
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