让自动驾驶赛车在遵守规则前提下，聪明地超车
Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing
- 用规则约束建模车辆行为，形成博弈关系
- 仿真显示新方法超车更有效且始终合规
- 适合追求安全高效竞速策略的研究者
本文提出一种面向自动驾驶赛车场景的规则感知运动规划框架。每个智能体求解一个规则合规的模型预测控制问题，通过混合逻辑动态约束编码竞速规则，如优先权和避碰责任。将车辆间交互形式化为广义纳什均衡问题，并采用迭代最优响应法近似求解。在此基础上，提出规则感知博弈论规划器（RA-GTP），使进攻方能够预判防守方的规则受限行为。该博弈层生成既安全又不保守的超车策略。仿真结果表明，相较于假设对手无互动或无视规则的基线方法，RA-GTP在保持规则合规的前提下，实现了更高效的驾驶操作。
原文摘要 · Abstract (English)
This paper presents a regulation-aware motion planning framework for autonomous racing scenarios. Each agent solves a Regulation-Compliant Model Predictive Control problem, where racing rules - such as right-of-way and collision avoidance responsibilities - are encoded using Mixed Logical Dynamical constraints. We formalize the interaction between vehicles as a Generalized Nash Equilibrium Problem (GNEP) and approximate its solution using an Iterative Best Response scheme. Building on this, we introduce the Regulation-Aware Game-Theoretic Planner (RA-GTP), in which the attacker reasons over the defender's regulation-constrained behavior. This game-theoretic layer enables the generation of overtaking strategies that are both safe and non-conservative. Simulation results demonstrate that the RA-GTP outperforms baseline methods that assume non-interacting or rule-agnostic opponent models, leading to more effective maneuvers while consistently maintaining compliance with racing regulations.
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