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

Abstract:

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.