Driver longitudinal car-following behavior model construction method based on deep reinforcement learning

Abstract

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.

Description

technical field [0001] The invention belongs to the field of automobile intelligent safety and automatic driving, and in particular relates to a method for constructing a driver's longitudinal car-following behavior model based on deep reinforcement learning. Background technique [0002] In the future, drivers will play an important role in the driving tasks of smart cars. In order to reduce the driver's driving burden, improve the driver's driving ability and acceptance of the intelligent driving system, it is necessary to conduct in-depth research on the driver's driving habits. Establishing a driver model that accurately reflects the driver's following behavior is of great significance for the development of control strategies for intelligent driving systems. [0003] In recent years, from different perspectives, such as traffic engineering perspective, human factors engineering perspective, etc., or based on different theories, using different research methods to study...

AI Technical Summary

Problems solved by technology

However, this model is made by linear or non-linear fitting based on the state information of the following car, which is difficult to truly reflect the randomness and complexity of the driver's following behavior.

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