The invention discloses a driver longitudinal car-following behavior model construction method based on deep
reinforcement learning, and belongs to the field of automobile intelligent safety and automatic driving. The method comprises the steps of based on the actual road working condition of China, collecting vehicle state information and surrounding environment information, meeting the road characteristics of China, of a driver in the
vehicle driving process, counting and analyzing the collected data, and giving behavior characteristics and influence factors of the driver in the
car following driving process; determining reference information representing actions taken by the driver at a certain moment, and establishing a
mathematical model for describing the iterative relationship of the driver car-following behavior state; designing a neural
network structure of the driver longitudinal car-following behavior model based on the competitive Q
network architecture; designing a driverlongitudinal car-following
behavior learning process of a neural network based on the competitive Q
network architecture; and designing a training method of the driver longitudinal vehicle following behavior model based on deep
reinforcement learning. The
car following behavior characteristics of the driver under different working conditions can be accurately described, and the
reproduction capability of the
car following behavior of the driver is achieved.