Cooperative Adaptive Cruise Control (CACC) represents a quintessential control strategy for orchestrating vehicular platoon movement within Connected and Automated Vehicle (CAV) systems, significantly enhancing traffic efficiency and reducing energy consumption. In recent years, the data-driven methods, such as reinforcement learning (RL), have been employed to address this task due to their significant advantages in terms of efficiency and flexibility. However, the delay issue, which often arises in real-world CACC systems, is rarely taken into account by current RL-based approaches. To tackle this problem, we propose a Delay-Aware Multi-Agent Reinforcement Learning (DAMARL) framework aimed at achieving safe and stable control for CACC. We model the entire decision-making process using a Multi-Agent Delay-Aware Markov Decision Process (MADA-MDP) and develop a centralized training with decentralized execution (CTDE) MARL framework for distributed control of CACC platoons. An attention mechanism-integrated policy network is introduced to enhance the performance of CAV communication and decision-making. Additionally, a velocity optimization model-based action filter is incorporated to further ensure the stability of the platoon. Experimental results across various delay conditions and platoon sizes demonstrate that our approach consistently outperforms baseline methods in terms of platoon safety, stability and overall performance.

翻译：协同自适应巡航控制（CACC）是网联自动驾驶车辆（CAV）系统中管理车辆队列运动的核心控制策略，能显著提升交通效率并降低能耗。近年来，强化学习（RL）等数据驱动方法因其在效率和灵活性方面的显著优势被应用于该任务。然而，现有基于RL的方法鲜少考虑真实CACC系统中普遍存在的延迟问题。针对这一挑战，我们提出一种延迟感知的多智能体强化学习（DAMARL）框架，旨在实现安全稳定的CACC控制。通过构建多智能体延迟感知马尔可夫决策过程（MADA-MDP）对整个决策过程建模，并开发了集中式训练与分布式执行（CTDE）的MARL框架以实现CACC队列的分布式控制。引入集成注意力机制的策略网络以增强CAV通信与决策性能，同时结合基于速度优化模型的动作滤波器进一步保障队列稳定性。在多种延迟条件与队列规模下的实验结果表明，本方法在队列安全性、稳定性和整体性能方面始终优于基线方法。