Analyzing Driving Safety on Prairie Highways: A Study of Drivers’ Visual Search Behavior in Varying Traffic Environments
Xu Ding, Haixiao Wang, Chutong Wang, Min Guo
Abstract
This study aimed to investigate disparities in drivers’ visual search behavior across various typical traffic conditions on prairie highways and analyze driving safety at the visual search level. The study captured eye movement data from drivers across six real-world traffic environments: free driving, vehicle-following, oncoming vehicles, rear vehicles overtaking cut-in, roadside risks, and driving through intersections, by carrying out a real vehicle test on a prairie highway. The drivers’ visual search area was divided into five areas using clustering principles. By integrating the Markov chain and information entropy theory, the information entropy of fixation distribution (IEFD) was constructed to quantify the complexity of drivers’ traffic information search. Additionally, the main area of visual search (MAVS) and the peak-to-average ratio of saccade velocity (PARSV) were introduced to measure visual search range and stability, respectively. The study culminated in the creation of a visual search load evaluation model that utilizes both VIKOR and improved CRITIC methodologies. The findings indicated that while drivers’ visual distribution and transfer modes vary across different prairie highway traffic environments, the current lane consistently remained their primary area of search for traffic information. Furthermore, it was found that each visual search indicator displayed significant statistical differences as traffic environments changed. Particularly when encountering roadside risks, drivers’ visual search load increased significantly, leading to a considerable decrease in driving safety.