文献类型：期刊文献

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, Jun

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China; [2] College of Robotics, Beijing Union University, Beijing, 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

年份：2021

起止页码：136-140

外文期刊名：ICEIEC 2021 - Proceedings of 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication

收录：EI(收录号：20213510835941)

语种：英文

外文关键词：Deep learning - Reinforcement learning

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 e-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. ? 2021 IEEE.