基于博弈论的车辆轨迹规划，提升自动驾驶安全性与计算效率。
A Semi-decentralized and Variational-Equilibrium-Based Trajectory Planner for Connected and Autonomous Vehicles
- 将车辆轨迹规划建模为带安全约束的博弈，求解变分均衡解。
- 实测显示规划速度更快、可扩展性强，且多车协同更安全。
- 适合大规模自动驾驶车队协同场景，尤其注重实时性与安全性。
本文设计了一种新型轨迹规划方法，利用车联网（V2X）技术解决传统非协调方法中存在的计算效率低和安全性差的问题。将联网自动驾驶车辆（CAVs）的轨迹规划问题建模为带有耦合安全约束的博弈，定义了交互公平轨迹，并证明其对应于该博弈的变分均衡（VE）。提出一种半去中心化规划器，使车辆能够并行计算以寻求基于VE的公平轨迹，显著提升计算效率，并通过确保各车辆间均衡一致性增强轨迹安全性。实验结果表明，该方法具备快速计算、高可扩展性、均衡一致性及安全性优势。
原文摘要 · Abstract (English)
This paper designs a novel trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated methods by exploiting vehicle-to-everything (V2X) technology. The trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define interaction-fair trajectories and prove that they correspond to the variational equilibrium (VE) of this game. We propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, which can significantly improve computational efficiency through parallel computing among CAVs and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, experimental results show the advantages of the approach, including fast computation speed, high scalability, equilibrium concordance, and safety.
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