SGTP：基於採樣的博弈理論規劃於即時多車輛自動駕駛賽車之應用

摘要

自動駕駛多車輛競速需要在激烈互動中即時規劃多樣化的競爭行為。現有規劃器時常在策略多樣性與計算效率之間難以兼顧。為應對此挑戰，我們提出基於取樣的賽局理論規劃（Sampling-based Game-Theoretic Planning, SGTP），這是一個即時框架，結合賽局理論推理與GPU加速的控制序列取樣及動力學展開。取樣的軌跡透過賽局感知成本函數進行排序，以捕捉競爭互動並產生多樣化的競速行為。我們的規劃器隨後透過明確執行賽道邊界與動態碰撞避免約束來進行可行性篩選，確保競速策略之間安全且可靠的轉換。在具挑戰性的賽道上進行的廣泛模擬顯示，SGTP在高度互動的競賽中達成95.24%的勝率與99.35%的任務完成率，並在多個迭代求解步驟中達到平均0.095秒的計算時間。我們亦展示SGTP在最多10個智能體的大規模場景中的成功應用。我們釋出程式碼，並提供多智能體自動駕駛競速演算法的開源基準測試，以促進未來研究。專案頁面：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/.