Drivers’ visual scanning behavior at signalized and unsignalized intersections: A naturalistic driving study in China

• DOI: 10.1016/j.jsr.2019.09.012
• Publication Date: 2019-11-20T16:41:52Z
• Authors: Guofa Li; Ying Wang; Fangping Zhu; Xiaoxuan Sui; Ning Wang; Xingda Qu; Paul Green
• Keywords: Adult; Male; Automobile Driving; China; Eye Movements; Accidents, Traffic; 02 engineering and technology; Middle Aged; Young Adult; Dangerous Behavior; 11. Sustainability; 0202 electrical engineering, electronic engineering, information engineering; Humans; Environment Design; Female

Intersections are the most dangerous locations in urban traffic. The present study aims to investigate drivers' visual scanning behavior at signalized and unsignalized intersections.Naturalistic driving data at 318 green phase signalized intersections and 300 unsignalized ones were collected. Drivers' glance allocations were manually categorized into 10 areas of interest (AOIs), based on which three feature subsets were extracted including glance allocation frequencies, durations and AOI transition probabilities. The extracted features at signalized and unsignalized intersections were compared. Features with statistical significances were integrated to characterize drivers' scanning patterns using the hierarchical clustering method. Andrews Curve was adopted to visually illustrate the clustering results of high-dimensional data.Results showed that drivers going straight across signalized intersections had more often glances at the left view mirror and longer fixation on the near left area. When turning left, drivers near signalized intersections had more frequent glances at the left view mirror, fixated much longer on the forward and rearview mirror area, and had higher transition probabilities from near left to far left. Compared with drivers' scanning patterns in left turning maneuver at signalized intersections, drivers with higher situation awareness levels would divide more attention to the forward and right areas than at unsignalized intersections.This study revealed that intersection types made differences on drivers' scanning behavior. Practical applications: These findings suggest that future applications in advanced driver assistance systems and driver training programs should recommend different scanning strategies to drivers at different types of intersections.