发表机构
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich; Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑工业大学工程与设计学院自动驾驶车辆系统教授组; 慕尼黑机器人与机器智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主赛车在多智能体场景下的规划问题，提出将博弈论推理集成到基于采样的运动规划器的混合架构，利用离线学习势函数和在线梯度优化生成交互参考路径，在模拟环境中验证该方法能诱导防御行为且无计算负担。
AI 中文摘要
自主赛车需要规划算法，在操控极限下平衡车辆动力学与竞争多智能体场景中的战略决策。博弈论为建模这些交互提供数学框架，实现交互式轨迹规划和战略行为，如阻挡。然而，在线直接求解完整动态博弈在计算上令人望而却步，且难以集成到强大的高频自主软件栈中。本文提出一种混合架构，将博弈论推理集成到基于采样的运动规划器中，结合战略交互与强大的轨迹生成。基于α - 势博弈公式，利用离线学习的势函数捕捉多智能体交互。在线运行时，基于梯度的优化动态细化交互参数以生成“交互参考路径”。此路径在高频采样规划器中用作动态成本偏差。我们在亚斯码头赛道的高保真模拟环境中评估方法。定性和定量结果表明，我们的方法成功诱导出如阻挡等防御性行为，而无需承担完整动态博弈求解器的计算负担。
英文摘要
Autonomous racing demands planning algorithms that balance vehicle dynamics at the limits of handling with strategic decision-making in competitive multi-agent scenarios. Game theory provides a mathematical framework for modeling these interactions, enabling interactive trajectory planning and strategic behaviors, such as blocking. However, directly solving full dynamic games online is computationally prohibitive and challenging to integrate into robust, high-frequency autonomous software stacks. This paper proposes a hybrid architecture that integrates game-theoretic reasoning into a sampling-based motion planner, combining strategic interactions with robust trajectory generation. Building upon an $α$-potential game formulation, we utilize an offline-learned potential function to capture multi-agent interactions. During online operation, a gradient-based optimization dynamically refines interaction parameters to generate an \textit{Interaction Reference Path}. This path serves as a dynamic cost bias within a high-frequency sampling planner. We evaluate our approach in a high-fidelity simulation environment on the Yas Marina Circuit. Qualitative and quantitative results demonstrate that our approach successfully induces defensive behaviors like blocking without carrying the computational burden of full dynamic game solvers.
CommentsAccepted to IEEE ITSC 2026
