Trajectory Optimization for Automated Vehicles of Different Cooperation Classes Using Reinforcement Learning at a Signalized Intersection

Existing studies on trajectory optimization for cooperative automated driving systems (C-ADS) equipped vehicles at signalized intersections operate under a simplified assumption of cooperative behaviour: all vehicles accept and follow to the prescribed plans. To investigate trajectory optimization for C-ADS-equipped vehicles with different cooperation classes, a deep deterministic policy gradient (DDPG) algorithm was developed within a reinforcement learning (RL) framework, alongside baseline implementations of trajectory smoothing (TS)-based C-ADS systems and human-driven vehicle scenarios. Experimental results indicate that the proposed methodology achieves significant reductions in average travel time (53.59%) and stop times, compared to benchmark approaches. Furthermore, novel insights into the performance improvements at signalized intersections were derived from analysing different cooperation classes of C-ADS-equipped vehicles via the RL model, providing critical guidance for refining control strategies in cooperative automated driving systems. This study validates that RL models utilizing the DDPG algorithm serve as effective tools for enhancing the performance of cooperative automated driving systems.