Cooperative On-Ramp Merging Control of Connected and Automated Vehicles: Distributed Multi-Agent Deep Reinforcement Learning Approach
Shanxing Zhou, Weichao Zhuang, Guodong Yin, Haoji Liu, Chunlong Qiu
Abstract
This paper proposes a cooperative merging control strategy of connected and automated vehicles (CAVs) using distributed multi-agent Deep Deterministic Policy Gradient (MADDPG). First, the on-ramp merging scenario and vehicle model are built, considering the safe merging distances and acceleration limits. Second, the MADDPG is adopted to learn the cooperative control strategy considering the rear-end safety, lateral safety, and vehicle energy consumption. A distributed architecture is proposed to improve training efficiency. Finally, several on-ramp merging scenarios are simulated. Simulation results show that the distributed MADDPG merging strategy reduces energy consumption by 7.4% and travel time by 5.3% compared to the regular merging strategy.