基于联邦强化学习的智能变道系统，按目的地紧急程度优先决策。
PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learning
- 用联邦强化学习实现多车协同变道，按目的地紧迫性分配优先级。
- 相比基线方法，变道成功率提升18%，到达率提高23%。
- 适合自动驾驶车队在复杂交通中提升安全与效率，尤其适用于车联网场景。
我们提出一种基于多智能体联邦强化学习的优先级感知智能变道建议系统PALCAS，用于自动驾驶车辆（AV）。现有变道方法多聚焦于单智能体或集中式多智能体系统，而PALCAS引入基于目的地紧急程度的优先级感知安全变道奖励函数，在强制和非强制变道场景下均能做出合理决策。系统采用参数化深度Q网络（PDQN）算法，实现智能体间有效协作，支持车辆横向与纵向运动控制。通过SUMO交通仿真器与Mosaic V2X通信框架开展的大量仿真实验表明，相较于基线方法，PALCAS显著提升了交通效率、驾驶安全、乘坐舒适度、目的地到达率及汇入成功率。
原文摘要 · Abstract (English)
We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomous vehicles (AVs). While existing lane-change approaches typically focus on single-agent systems or centralized multi-agent systems, we introduce a federated reinforcement learning-based multi-agent lane change system prioritizing lane changing based on vehicle destination urgency. PALCAS incorporates a novel priority-aware safe lane-change reward function to enable judicious lane-change decisions in both mandatory and discretionary scenarios. PALCAS leverages the parameterized deep Q-network (PDQN) algorithm to facilitate effective cooperation among agents, enabling both lateral and longitudinal motion controls of AVs. Extensive simulations conducted using the SUMO traffic simulator and Mosaic V2X communication framework demonstrate that PALCAS significantly improves traffic efficiency, driving safety, comfort, destination arrival rates, and merging success rates compared to baseline methods.
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