用博弈均衡建模自动驾驶与人类司机的实时互动，更安全自然。
A Real-Time Generalized Nash Equilibrium Framework for Interaction-Aware Autonomous Driving in Mixed Traffic
- 将驾驶交互建模为广义纳什均衡，考虑双方行为相互影响。
- 实测求解器50毫秒内收敛，生成类人舒适轨迹。
- 适合追求高安全、真实交互体验的自动驾驶研发者。
在混合交通环境中，自动驾驶车辆（AV）的安全高效导航仍面临严峻挑战，主要源于AV决策与人类驾驶员不可预测反应之间的复杂相互依赖。本文提出一种综合决策框架，将驾驶交互建模为广义纳什均衡问题（GNEP）。与解耦优化方法不同，该框架显式建模共享安全与几何约束，确保AV策略可行性动态依赖于对方行为。为实现实时求解，提出基于粒子群优化（PSO）的专用求解器。完整系统在测试场通过一辆真实自主雷诺Zoé与人类驾驶员交互验证。实验结果表明，系统能有效处理关键场景，生成舒适、类人轨迹。基准测试证实求解器具备运行可行性，收敛时间低于50毫秒。
原文摘要 · Abstract (English)
Safe and efficient navigation in mixed-traffic environments remains a critical challenge for Autonomous Vehicles (AVs), primarily due to the complex interdependence between the AV's decisions and the unpredictable reactions of human drivers. This paper introduces a comprehensive decision-making framework that formulates the driving interaction as a Generalized Nash Equilibrium Problem (GNEP). Unlike decoupled optimization approaches, this framework explicitly models shared safety and geometric constraints, ensuring that the feasibility of the AV's strategy is dynamically linked to the opponent's actions. To solve this non-convex problem in real-time, we propose a dedicated solver based on Particle Swarm Optimization (PSO). The complete architecture was validated on a test track using a real autonomous Renault Zoé interacting with a human driver. Experimental results demonstrate the system's ability to handle critical scenarios by generating comfortable, human-like trajectories. Benchmarks confirm the solver's operational feasibility, achieving convergence in under 50 ms.
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