Analysis of Normal Stopping Behavior of Drivers at Urban Intersections in China

Abstract

Driving behavior analysis plays an important role in improving the human-like driving capability of the Advanced Driver Assistance System (ADAS) and autonomous driving technologies. In this study, 906 data of drivers stopping normally at urban intersections were extracted from natural driving data to study reaction characteristics and braking characteristics of drivers in real road traffic. The effects of traffic state and traffic density on approach speed and reaction distance were studied using one-way ANOVA, and the relationships between maximum deceleration, average deceleration, braking duration, and influencing factors were analyzed using hierarchical multiple regression. The main results indicate the following: (1) compare with free driving, the approach speed in the following state was lower, and following traffic also caused drivers with higher speeds to further increase reaction distance in high-density traffic. (2) Traffic density only affected the approach speed in the free-driving condition. High-density traffic caused drivers to reduce speed. (3) There was a clear correlation between approach speed and reaction distance: the greater the approach speed, the longer the reaction distance. (4) The braking characteristics were mainly affected by the self-vehicle motion state. Drivers braked to a stop with greater average deceleration and maximum deceleration in a shorter time when the approach speed was higher or the reaction distance was shorter. (5) Both traffic state and traffic density had an influence on the braking characteristics. Drivers reduced average deceleration and increased braking duration in the following state and high-density traffic. (6) When following a preceding vehicle to stop, the braking characteristics of drivers were no longer influenced by the traffic density but were related to the relative motion state with the preceding vehicle. In addition to the driving behavior analysis, the identification method of traffic participants and ranging method used in this study will also advance the development of autonomous driving technologies and driver assistance systems.