Deep reinforcement learning augmented decision⁃making model for intelligent driving vehicles

A deep reinforcement learning agent based on Deep Q-Network （DQN） algorithm was constructed to solve the problem that the state machine decision model cannot effectively deal with the rich context information and the influence of uncertain factors in the snow and ice environment. The motion planner was used to augment the agent，and the rule-based decision planning module and the deep reinforcement learning model were integrated together to build the DQN-planner model，so as to improve the convergence speed and driving ability of the reinforcement learning agent. Finally，the driving ability of DQN model and DQN-planner on ice and snow road with low adhesion coefficient is compared based on CARLA simulation platform，and the training process and verification results are analyzed respectively. © 2023 Editorial Board of Jilin University. All rights reserved.