AI 中文总结
针对自动驾驶在混合交通环境中的挑战，提出将驾驶交互建模为广义纳什均衡问题的决策框架，基于粒子群优化提出实时求解器，经实验验证能处理关键场景，求解器操作可行且收敛快。
AI 中文摘要
在混合交通环境中实现安全高效的导航对自动驾驶车辆来说仍是一项关键挑战，主要是因为自动驾驶车辆的决策与人类驾驶员不可预测的反应之间存在复杂的相互依赖关系。本文引入了一个全面的决策框架，将驾驶交互表述为广义纳什均衡问题（GNEP）。与解耦优化方法不同，该框架明确对共享安全和几何约束进行建模，确保自动驾驶车辆策略的可行性与对手的行动动态相关。为实时解决这个非凸问题，我们提出了一种基于粒子群优化（PSO）的专用求解器。完整架构在测试轨道上通过一辆真实的自动驾驶雷诺佐伊与人类驾驶员交互进行了验证。实验结果表明该系统能够通过生成舒适、类人的轨迹来处理关键场景。基准测试证实了求解器的操作可行性，在50毫秒内实现收敛。
英文摘要
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.
Journal refThe 12th 2026 International Conference on Control, Decision and Information Technologies (CoDIT 2026), Jul 2026, Bari (Italy), Italy
