  Connected vehicles will change the modes of future transportation management
and organization, especially at an intersection without traffic light.
Centralized coordination methods globally coordinate vehicles approaching the
intersection from all sections by considering their states altogether. However,
they need substantial computation resources since they own a centralized
controller to optimize the trajectories for all approaching vehicles in
real-time. In this paper, we propose a centralized coordination scheme of
automated vehicles at an intersection without traffic signals using
reinforcement learning (RL) to address low computation efficiency suffered by
current centralized coordination methods. We first propose an RL training
algorithm, model accelerated proximal policy optimization (MA-PPO), which
incorporates a prior model into proximal policy optimization (PPO) algorithm to
accelerate the learning process in terms of sample efficiency. Then we present
the design of state, action and reward to formulate centralized coordination as
an RL problem. Finally, we train a coordinate policy in a simulation setting
and compare computing time and traffic efficiency with a coordination scheme
based on model predictive control (MPC) method. Results show that our method
spends only 1/400 of the computing time of MPC and increase the efficiency of
the intersection by 4.5 times.
