Research on formation vehicles cooperative strategy based on reinforcement learning and GANs at uncontrolled...

This paper presents an innovative model of safe driving for formation vehicles while passing potential traffic accident areas and a special strategy to optimize the velocity and acceleration based on Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). The concept of occluded scenes and uncontrolled intersections are described and the parameters are detailed. The evasion mechanism underlying risk-aware driving is systematically analyzed. Based on this analysis, a safety-oriented driving strategy for occluded scenarios is proposed to generate preliminary yet reliable reference velocity and acceleration for formation vehicles. Reinforcement learning model for uncontrolled intersection is trained and tested to optimize the formation driving strategy. Furthermore, generative adversarial networks is used to enrich driving scenarios and enhance the safety and efficiency of strategies presented. Experimental results in simulation show that the proposed formation vehicles driving strategy can improve the stability of formation when facing uncontrolled intersections.