用物理启发的博弈模型实现高效安全的自动驾驶轨迹规划
Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
- 基于平均场博弈构建统一势场，融合行为风格的收益与风险
- 仿真中实现安全距离、平滑轨迹，且计算效率优于传统方法
- 适合需要实时交互决策的自动驾驶系统开发
自动驾驶中的交互式轨迹规划需在异构驾驶行为下兼顾安全性、效率与可扩展性。现有方法常面临计算成本高或依赖外部安全判别器的问题。为此，我们提出一种交互增强型统一势场（IUPF）框架，通过物理启发的变分模型融合风格相关的收益与风险场，基于平均场博弈理论构建。该方法无需额外安全模块即可捕捉保守、激进和合作等行为，并利用随机微分方程保证纳什均衡及指数收敛。在变道与超车场景的仿真中，IUPF确保了安全距离，生成平滑高效的轨迹，在适应性和计算效率上均优于传统优化与博弈论基线方法。
原文摘要 · Abstract (English)
Interactive trajectory planning in autonomous driving must balance safety, efficiency, and scalability under heterogeneous driving behaviors. Existing methods often face high computational cost or rely on external safety critics. To address this, we propose an Interaction-Enriched Unified Potential Field (IUPF) framework that fuses style-dependent benefit and risk fields through a physics-inspired variational model, grounded in mean field game theory. The approach captures conservative, aggressive, and cooperative behaviors without additional safety modules, and employs stochastic differential equations to guarantee Nash equilibrium with exponential convergence. Simulations on lane changing and overtaking scenarios show that IUPF ensures safe distances, generates smooth and efficient trajectories, and outperforms traditional optimization and game-theoretic baselines in both adaptability and computational efficiency.
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