Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment

In order to reduce the number of vehicle collisions and average travel time when vehicles pass through an unsignalized intersection with connected and automated vehicle, an improved Double Dueling Deep Q Network method with Convolutional Neutral Network and Long Short-Term Memory is presented in this article. This method designs a multi-step reward and penalty method to alleviate the sparse reward problem using positive and negative reward experience replay buffer. The proposed method is validated in a simulation environment with different traffic flow and market penetration under the mixed traffic conditions of automated vehicles and human-driving vehicles. The results show that compared with traditional signal control methods, the proposed method can effectively improve the convergence and stability of the algorithm, reduce the number of collisions, and reduce the average travel time under different traffic conditions.