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

A technology of reinforcement learning and construction methods, applied in the direction of neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of randomness and complexity of car-following behaviors of difficult drivers, and achieve the effect of realizing reproducibility

Active Publication Date: 2021-01-08
XIAMEN UNIV
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Problems solved by technology

However, this model is made by linear or non-linear fitting based on the state information of the following

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

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Embodiment Construction

[0038] The following embodiments will further illustrate the present invention in conjunction with the accompanying drawings.

[0039] The schematic flow chart of the driver's longitudinal car-following behavior model of the present invention is as follows: figure 1 shown. Firstly, collect the data of the driver’s car-following behavior that conforms to the characteristics of Chinese roads, and give the key parameters representing the benchmark information of the driver’s behavior. Secondly, build a deep neural network structure of the driver’s longitudinal car-following behavior model to effectively solve the problem of driver’s car-following behavior. The decision-making problem in the continuous action space in the behavior process, and then, the training method of the driver's longitudinal car-following behavior model based on deep reinforcement learning is designed to realize the verification and evaluation of the driver's longitudinal car-following behavior model. The s...

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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...

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Application Information

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IPC IPC(8): G08G1/0967G06N3/08G06N3/04
CPCG08G1/096725G06N3/08G06N3/048G06N3/045
Inventor 郭景华李文昌王靖瑶王班肖宝平
Owner XIAMEN UNIV
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