A Collaborative Control Scheme for Smart Vehicles Based on Multi-Agent Deep Reinforcement Learning

With the development of artificial intelligence and autonomous driving technology, the vehicle-road cooperative control system combined with artificial intelligence technology can provide more effective and adaptive traffic control solutions for intelligent transportation systems. Existing research works are confronted with two kinds of challenges. For one thing, traditional recurrent neural networks-based methods cannot model the long-time dependent information in traffic flow sequences. For another, the large sample correlation makes it difficult to optimize the trained strategies. In this paper, we propose a Multi-agent Deep Reinforcement Learning (MADRL)-based intelligent vehicle cooperative control method to deal remedy current gaps. To this end, a closed-loop control system of self-driving vehicles and signal controllers is used as the research object to achieve dynamic scheduling of traffic flow by MADRL. After designing relevant experimental validation, the feasibility of the method is verified in terms of both scheme comparison and operational effect analysis, which is a good aid to traffic signal timing. The simulation results show that the proposal can be well utilized to realize collaborative control of smart vehicles, and there is some performance improvement compared with several typical methods.