|Table of Contents|

Human-machine integration method for steering decision-making of intelligent vehicle based on reinforcement learning(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年03期
Page:
55-67
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Human-machine integration method for steering decision-making of intelligent vehicle based on reinforcement learning
Author(s):
WU Chao-zhong1;  LENG Yao1; 2;  CHEN Zhi-jun1; 3;  LUO Peng1; 2
(1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China; 2. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China; 3. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430063, Hubei, China)
Keywords:
intelligent vehicle;  human-machine integration;  steering decision-making;  driving weight allocation;  reinforcement learning
PACS:
U461.9
DOI:
10.19818/j.cnki.1671-1637.2022.03.004
Abstract:
In terms of the continuous dynamic allocation problem of driving weights between human and autonomous driving systems in the human-machine integration(HMI)driving system of intelligent vehicles, especially the low adaptability problem of weight allocation methods caused by modeling errors, a HMI steering decision-making method based on the reinforcement learning was proposed. In view of drivers' steering characteristics, a driver model based on the two-point preview was built, and an autonomous steering control model of intelligent vehicles was established by adopting the predictive control theory. On this basis, a steering control framework of simultaneous human-machine in-loop for intelligent vehicles was constructed. According to the Actor-Critic reinforcement learning framework, a deep deterministic policy gradient(DDPG)agent for the human-machine driving weight allocation was designed, and a model-based gain function was proposed with the curvature adaptability, tracking accuracy, and ride comfort as targets. A reinforcement learning framework for the HMI driving weight allocation was constructed, which contains a driver model, an autonomous steering model, a driving weight allocation agent, and a gain function. To verify the effectiveness of the proposed method, eight drivers were recruited, and a total of 48 simulated driving experiments were carried out. Research results show that in the verification of curvature adaptability, the HMI-DDPG method is superior to the manned driving and HMI-Fuzzy methods. The trackability improves by an average of 70.69% and 39.67%, respectively, and the comfortability increases by an average of 18.34% and 7.55%, respectively. In the verification of speed adaptability, under the conditions of a vehicle speed of 40, 60, and 80 km·h-1, the time proportion is 90.00%, 85.76%, and 60.74%, respectively, when the driver's weight is greater than 0.5. The phase trajectories of both the trackability and the comfort can effectively converge. Therefore, the proposed method can adapt to changes in curvature and vehicle speed and improve the trackability and comfort on the premise of ensuring safety. 5 tabs, 14 figs, 31 refs.

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Last Update: 2022-07-20