Study of the Drivers' Visual Information in Non-Light Controlled Intersections Based on the Eye Tracker
Deli Han, Liu Don, Yan Zhao · CICTP 2017 · 2018
According to the geometric and traffic characteristics of non-light controlled intersections, this paper uses the FaceLAB eye tracker to capture the dynamic visual data in the process of driving; it uses Eyeworks Analysis, which is a kind of software that analyzes the number of drivers' fixation points, the position of the fixation points in the horizontal direction, eye movement indices, fixation points tracking videos, and other parameters. What's more, it can offer the visual information characteristics of the non-light controlled intersections by using clustering statistical analysis through Excel and R Program. As the experimental results suggest, there exist certain differences in the distribution breadth and density of the drivers' fixation points and focus area when influenced by different traffic conditions and visual information. Meanwhile, there is also identity in practical cases. In addition, dynamic, static, and composite visual information interference will also impact drivers' visual information characteristics. Via clustering analysis, we have also concluded that drivers' proficiency and vision level have a greater significant impact on their visual information characteristics.