title: Driving-simulator-based test on the effectiveness of auditory red-light running vehicle warning system based on time-to-collision sensor.
abstract: The collision avoidance warning system is an emerging technology designed to assist drivers in avoiding red-light running (RLR) collisions at intersections. The aim of this paper is to evaluate the effect of auditory warning information on collision avoidance behaviors in the RLR pre-crash scenarios and further to examine the casual relationships among the relevant factors. A driving-simulator-based experiment was designed and conducted with 50 participants. The data from the experiments were analyzed by approaches of ANOVA and structural equation modeling (SEM). The collisions avoidance related variables were measured in terms of brake reaction time (BRT), maximum deceleration and lane deviation in this study. It was found that the collision avoidance warning system can result in smaller collision rates compared to the without-warning condition and lead to shorter reaction times, larger maximum deceleration and less lane deviation. Furthermore, the SEM analysis illustrate that the audio warning information in fact has both direct and indirect effect on occurrence of collisions, and the indirect effect plays a more important role on collision avoidance than the direct effect. Essentially, the auditory warning information can assist drivers in detecting the RLR vehicles in a timely manner, thus providing drivers more adequate time and space to decelerate to avoid collisions with the conflicting vehicles.
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. xdyan@bjtu.edu.cn.
authors.forename: Xuedong
authors.initials: X
authors.lastname: Yan
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. 12120932@bjtu.edu.cn.
authors.forename: Qingwan
authors.initials: Q
authors.lastname: Xue
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. lma@bjtu.edu.cn.
authors.forename: Lu
authors.initials: L
authors.lastname: Ma
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. 12114242@bjtu.edu.cn.
authors.forename: Yongcun
authors.initials: Y
authors.lastname: Xu