Car-following Behavior Modeling Based on Uncertainty of Driver Distance Perception

被引：0

作者：

Wu B. ^{[1
]}

Liu Y. ^{[1
]}

Ni D. ^{[2
]}

Wang W. ^{[1
]}

Li L. ^{[1
]}

机构：

[1] Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai

[2] College of Engineering, University of Massachusetts Amherst, Amherst

来源：

Tongji Daxue Xuebao/Journal of Tongji University | 2021年 / 49卷 / 07期

关键词：

Car-following behavior; Longitudinal control model (LCM); Normal distribution; Uncertainty of distance perception; Uniform distribution;

D O I：

10.11908/j.issn.0253-374x.21159

中图分类号：

学科分类号：

摘要：

By taking the longitudinal control model (LCM) as the basic car-following model, the car-following behavior is modeled based on the uncertainty of driver distance perception. It is assumed that the error of perceptual spacing follows the uniform distribution and the truncated normal distribution. Thereafter, the relationships between the probability density function parameters (i.e. the boundary values of the uniform distribution, the mean value and standard deviation of the normal distribution) and the factors (i.e. observed spacing values and driver's aggressiveness characteristics) are established. Then, the extended model based on the uniform distribution and the extended model based on the truncated normal distribution are obtained.Finally, the Shanghai naturalistic driving data are employed to calibrate the extended models. It is shown that the extended models exhibit better performance than LCM, indicating that the extended models could capture the uncertainty of driver distance perception. Besides, the extended model based on the truncated normal distribution has better performance than the extended model based on the uniform distribution. © 2021, Editorial Department of Journal of Tongji University. All right reserved.

引用

页码：915 / 921

页数：6

共 18 条

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