文献类型：会议论文

英文题名：Research on Decision Model of Autonomous Vehicle Based on Deep Reinforcement Learning

作者：Zhang, Xinchen[1];Zhang, Jun[2];Lin, Yuansheng[2];Xie, Longyang[1]

第一作者：Zhang, Xinchen

通讯作者：Zhang, J[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

会议论文集：11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

会议日期：JUN 18-20, 2021

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：Deep Q Network; autonomous vehicle; lane changing decision; hybrid strategy

摘要：The Deep Q Network (DQN) model has been widely used in autonomous vehicle lane change decision in highway scenes, but the traditional DQN has the problems of overestimation and slow convergence speed. Aiming at these problems, an autonomous vehicle lane changing decision model based on the improved DQN is proposed. First, the obtained state values are input into two neural networks with the same structure and different parameter update frequencies to reduce the correlation between empirical samples, and then the hybrid strategy based on s-greedy and Boltzmann is used to make the vehicles explore the environment. Finally, the model is trained and tested in the experimental scene built by the NGSIM dataset. The experimental results show that the Double Deep Q Network (DDQN) model based on the hybrid strategy improves the success rate of the autonomous vehicle's lane-changing decision and the convergence speed of the network.