Autonomous vehicle scheduling at signal-free intersections based on deep reinforcement learning

Abstract

Coordinating autonomous vehicles (AVs) at signal-free intersections has emerged as a critical area of research in intelligent transportation. This paper presents a novel vehicle scheduling strategy based on multi-agent deep reinforcement learning (MADRL) to enhance traffic efficiency and reduce collision rates at signal-free intersections. The strategy incorporates the use of virtual lane techniques to simplify the management of vehicle trajectory conflicts. Furthermore, vehicle platooning techniques are employed to alleviate the computational burden on the reinforcement learning controller by treating multiple AVs as a single agent, thereby optimizing training performance. The MADRL approach is adopted to facilitate platoon coordination and enhance intersection traffic efficiency. Through comprehensive simulations, the proposed approach demonstrates its effectiveness in improving traffic efficiency, driving comfort, and safety at signal-free intersections.