Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization

Connected vehicles will change the modes of future transportation management and organization, especially at intersections. There are mainly two categories coordination methods at unsignalized intersection, i.e. centralized and distributed methods. Centralized coordination methods need huge computation resources since they own a centralized controller to optimize the trajectories for all approaching vehicles, while in distributed methods each approaching vehicles owns an individual controller to optimize the trajectory considering the motion information and the conflict relationship with its neighboring vehicles, which avoids huge computation but needs sophisticated manual design. In this paper, we propose a centralized conflict-free cooperation method for multiple connected vehicles at unsignalized intersection using reinforcement learning (RL) to address computation burden naturally by training offline. We firstly incorporate a prior model into proximal policy optimization (PPO) algorithm to accelerate learning process. Then we present the design of state, action and reward to formulate centralized cooperation as RL problem. Finally, we train a coordinate policy by our model-accelerated PPO (MA-PPO) in a simulation setting and analyze results. Results show that the method we propose improves the traffic efficiency of the intersection on the premise of ensuring no collision.