用多智能体强化学习优化自动驾驶车与信号灯协同，提升交通效率并省油。
HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning
- 分层控制：信号灯与车辆驾驶行为联合优化，采用价值分解网络实现分布式决策。
- 60%自动驾驶车比例下，平均车速提升7.67%，油耗降低10.23%，怠速减少45.83%。
- 适合关注智能交通、车联网和低碳出行的研究者与工程师参考。
本研究提出一种面向混合交通（人类驾驶车辆与联网自动驾驶车辆）的分层网络级交通流控制框架，联合优化车辆级节能驾驶行为与交叉口级信号灯控制，以提升整体网络效率并降低能耗。采用基于价值分解网络（VDN）的去中心化多智能体强化学习（MARL）方法管理基于周期的交通信号控制（TSC），同时创新性地结合机器学习轨迹规划算法（MLTPA）实现信号相位与配时（SPaT）预测，引导自动驾驶车辆执行节能接近与出发（EAD）操作。在4×4真实路网中评估不同自动驾驶车辆比例与动力系统类型下的性能表现。实验表明，相比基准模型（Webster法），该方法在速度、燃油消耗和怠速时间上均有显著改善。当自动驾驶车辆占比达60%时，平均车速提升7.67%，燃油消耗降低10.23%，怠速时间减少45.83%。此外，还分析了自动驾驶程度与电动化对系统性能的影响。
原文摘要 · Abstract (English)
This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle-level eco-driving behaviors and intersection-level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi-Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle-based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning-based Trajectory Planning Algorithm (MLTPA) to guide CAVs in executing Eco-Approach and Departure (EAD) maneuvers. The framework is evaluated across varying CAV proportions and powertrain types to assess its effects on mobility and energy performance. Experimental results conducted in a 4*4 real-world network demonstrate that the MARL-based TSC method outperforms the baseline model (i.e., Webster method) in speed, fuel consumption, and idling time. In addition, with MLTPA, HONEST-CAV benefits the traffic system further in energy consumption and idling time. With a 60% CAV proportion, vehicle average speed, fuel consumption, and idling time can be improved/saved by 7.67%, 10.23%, and 45.83% compared with the baseline. Furthermore, discussions on CAV proportions and powertrain types are conducted to quantify the performance of the proposed method with the impact of automation and electrification.
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