DRL-based Vehicle Control Strategy for Signal-free Intersections

Abstract

Using deep learning technology to control vehicles at intersections is a research hotspot in the field of intelligent transportation.Previous studies suffer from the inability to adapt to dynamic changes in the number of self-driving vehicles,slow convergence of training,and locally optimal training results.This work focuses on how autonomous vehicles can use distributed deep reinforcement methods to improve the efficiency of intersections at unsignalized intersections.First,an efficient reward function is proposed to apply the distributed reinforcement learning algorithm to the unsignalized intersection scenario,which can effectively improve the efficiency of intersection passage by relying on only local information even if the vehicle cannot obtain the whole intersection state information.Then,to address the problem of inefficient training of reinforcement learning methods in open intersection scenarios,a transfer learning approach is used to improve the training efficiency by using the trained strategy in the closed figure-of-eight scenario as a warm start and continuing the training in the unsignalized intersection scenario.Finally,this paper proposes a strategy that can be adapted to all proportions of autonomous vehicles,and this strategy can improve intersection access efficiency in scenarios with any proportion of autonomous vehicles.The algorithm is validated on the simulation platform Flow,and the experimental results show that the proposed smart body model converges quickly in training,can adapt to dynamic changes in the proportion of self-driving vehicles,and can effectively improve the efficiency of intersections.

Keywords

• autonomous vehicles|signal-free intersections|deep reinforcement learning|v2x