Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment

Many studies have been made to improve the AVs' ability of environment recognition and vehicle control, while the attention paid to decision making is not enough though the decision algorithms so far are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following (CF), is trained within this framework. In addition, theoretical analysis and experiments were conducted on setting reward function for accelerating training using DRL. The results show that on the premise of driving comfort, the efficiency of the trained AV increases 7.9% compared to the classical traffic model, intelligent driver model (IDM). Later on, on a more complex three-lane section, we trained the integrated model combines both CF and lane-changing (LC) behavior, the average speed further grows 2.4%. It indicates that our framework is effective for AV's decision-making learning. Keywords: Automated vehicle; Decision making; Deep reinforcement learning; Reward function 1. Introduction The automated vehicles have captured the public attention in recent years, especially after Google announced its automated driving program in 2010, for its advantages of alleviating the traffic congestion, liberating drivers' attention and conserving energy. The tasks involved in achieving autonomous driving can be divided into three modules: environment recognition, decision making and vehicle control. Among them, the vehicle control has no obvious differences between AV and manual driven vehicle.