Eco-cooperative adaptive cruise control for platoons in mixed traffic using single-agent and multi-agent reinforcement learning
研究了基于强化学习的生态协同自适应巡航控制方法，对比单智能体和多智能体算法在信号灯路段混合交通中的表现，发现DDPG和SAC在单车性能上最优，而MADDPG和MASAC在队列稳定性上最佳。
Signalized arterials create stop-and-go traffic, leading to collisions, delays, wasted energy, and discomfort. Connected Automated Vehicles (CAVs), using Cooperative Adaptive Cruise Control (CACC), can potentially mitigate these issues by optimizing speeds with shared information. However, the traffic environment in CACC research on signalized roads is predominantly generated through simulations. This paper compares various eco-friendly CACC methods based on reinforcement learning (RL) for CAVs operating with Human-driven Vehicles (HVs) on signalized arterials. Methods analyzed include Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), and their multi-agent versions (MADDPG, MASAC), trained and tested on naturalistic data from the pNEUMA dataset. These RL methods are benchmarked against human-driven trajectories and the Intelligent Driver Model (IDM) in mixed platoon scenarios. Results show that DDPG and SAC excel in vehicle performance (safety, efficiency, energy, comfort), while MADDPG and MASAC perform best in platoon stability. Key factors influencing performance include platoon characteristics, vehicle position, and preceding vehicle type.