title: Drivers' eye movements as a function of collision avoidance warning conditions in red light running scenarios.
abstract: The intersection collision avoidance warning systems (ICAWSs) have substantial potentials in improving driving performance and reducing the number and severity of intersection collisions, through helping drivers timely detect hazardous conflicting vehicles in precrash scenarios. However, the influences of ICAWS on drivers' visual performance have barely been discussed. This study focuses on exploring the patterns in drivers' eye movements as a function of ICAWS's warning conditions in red light running scenarios based on a driving simulation experiment. Two types of speech warning conditions including warning timings (varied form 2.5s to 5.5s) and directional information (with or without) are examined, and the no-warning condition is the baseline. The results revealed that more subjects would be likely to benefit from the ICWAS under the earlier warning timings. The warning condition of 4.5s ahead of a collision had the best effectiveness in terms of visual performances. Under such a warning timing, drivers had shorter fixation duration and higher frequency of searching for the red light running (RLR) vehicles. Compared to the warning condition without directional information, the directional warning information could capture drivers' attention more efficiently, help driver direct fixations toward the RLR vehicles more quickly and lead to more scanning activities. Compared to female drivers, male drivers had more scanning activities when approaching intersections, detected the RLR vehicles more quickly and were more likely to avoid the RLR collisions. Besides, the experiment results indicated that the female drivers were more inclined to trust the warning information and got more benefits from the RLR-ICAWS in terms of the crash risk reduction rate than male drivers. Finally, the conclusions lead the way toward warning condition design recommendations for improving the effectiveness of the RLR-ICAWSs.
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, PR China. Electronic address: 15114233@bjtu.edu.cn.
authors.forename: Yuting
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, PR China. Electronic address: 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, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, PR China. Electronic address: 13114249@bjtu.edu.cn.
authors.forename: Xiaomeng
authors.initials: X
authors.lastname: Li
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, PR China. Electronic address: 14114258@bjtu.edu.cn.
authors.forename: Qingwan
authors.initials: Q
authors.lastname: Xue
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D001288:Attention; D001334:Automobile Driving / Q000523:psychology*; D005133:Eye Movements / Q000502:physiology*; D005260:Female; D006801:Humans; D008027:Light; D008297:Male; D018570:Risk Assessment; D040242:Risk Reduction Behavior; D055815:Young Adult
keywords: Driving simulator; Eye movements; Intersection collision avoidance warning systems; Red light running; Warning conditions