Driver Response Mechanism in Connected Environment—A Case Study of Qualitative Effect Analysis and Quantitative Driving Pattern Extraction of Intersection-approaching Behavior

doi:10.3901/JME.2024.10.022

Abstract

Abstract: The development of intelligent connected technology has provided great opportunities for the improvement of traffic safety and efficiency. However, the existing research fails to elaborate the driver’s cognitive response mechanism to the environment in the connected environment, and lacks the quantitative analysis of driving patterns in the connected environment. A method for studying the intersection-approaching behavior process and response mechanism is proposed, and the traffic behavior mechanism of drivers in the connected environment is explored. Two driving scenarios are designed in the driving simulator, namely the benchmark traditional environment and the controlled connected environment. In the connected environment, the driver is provided with the traffic light phase and the remaining time of the current phase state. Parameters such as visual interaction information, vehicle kinematics, and driver operating behavior characteristics of 34 drivers are collected. The interaction frequency and cumulative time percentage of the human-machine interface, the first interaction time and response time, and the behavioral characteristics of drivers approaching intersections are analyzed. A driving pattern extraction model based on bayesian non-parametric method combined with text clustering algorithm is established to achieve quantitative description of driving patterns. The results show that there are significant differences in the human-machine interaction characteristics under red and green light phases, and the first interaction time and response time are highly correlated. The connected environment can significantly improve the efficiency of intersection traffic and improve driving behavior. The proposed driving model can effectively describe the six driving patterns of intersection-approaching behaviors, and the connected environment can reduce the acceleration behavior by 23.7%, and increase the smooth driving ratio by 25.0%.

Key words: automotive engineering, driving behavior, human-machine interaction, driving pattern, intelligent connected vehicle

CLC Number:

B842
U461

ZHANG Hailun, XU Qing, GAO Bolin, WANG Jianqiang, LI Keqiang. Driver Response Mechanism in Connected Environment—A Case Study of Qualitative Effect Analysis and Quantitative Driving Pattern Extraction of Intersection-approaching Behavior[J]. Journal of Mechanical Engineering, 2024, 60(10): 22-47.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2024.10.022

http://www.cjmenet.com.cn/EN/Y2024/V60/I10/22

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