This paper proposes a deep reinforcement learning (DRL)-based distributed longitudinal control strategy for connected and automated vehicles (CAVs) under communication failure to stabilize traffic oscillations. Specifically, the signal-interference-plus-noise ratio-based vehicle-to-vehicle communication is incorporated into the DRL training environment to reproduce the realistic communication and time–space varying information flow topologies (IFTs). A dynamic information fusion mechanism is designed to smooth the high-jerk control signal caused by the dynamic IFTs. Based on that, each CAV controlled by the DRL-based agent was developed to receive the real-time downstream CAVs’ state information and take longitudinal actions to achieve the equilibrium consensus in the multi-agent system. Simulated experiments are conducted to tune the communication adjustment mechanism and further validate the control performance, oscillation dampening performance and generalization capability of our proposed algorithm.

中文翻译：

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通信故障下基于深度强化学习的分布式联网自动车辆控制

本文提出了一种基于深度强化学习 (DRL) 的分布式纵向控制策略，用于在通信故障情况下连接和自动驾驶车辆 (CAV) 以稳定交通振荡。具体来说，将基于信号干扰加噪声比的车对车通信纳入 DRL 训练环境，以重现真实的通信和时空变化的信息流拓扑 (IFT)。设计了一种动态信息融合机制来平滑由动态 IFT 引起的高抖动控制信号。在此基础上，开发了由基于 DRL 的代理控制的每个 CAV，以接收实时下游 CAV 的状态信息并采取纵向行动以实现多代理系统中的均衡共识。