Abstract
Driver hazard perception is highly related to involvement in traffic accidents, and vision is the most important sense with which we perceive risk. Therefore, it is of great significance to explore the characteristics of drivers’ eye movements to promote road safety. This study focuses on analyzing the changes of drivers’ eye-movement characteristics in anxiety. We used various materials to induce drivers’ anxiety, and then conducted the real driving experiments and driving simulations to collect drivers’ eye-movement data. Then, we compared the differences between calm and anxiety on drivers’ eye-movement characteristics, in order to extract the key eye-movement features. The least squares method of change point analysis was carried out to detect the time and locations of sudden changes in eye movement characteristics. The results show that the least squares method is effective for identifying eye-movement changes of female drivers in anxiety. It was also found that changes in road environments could cause a significant increase in fixation count and fixation duration for female drivers, such as in scenes with traffic accidents or sharp curves. The findings of this study can be used to recognize unexpected events in road environment and improve the geometric design of curved roads. This study can also be used to develop active driving warning systems and intelligent human–machine interactions in vehicles. This study would be of great theoretical significance and application value for improving road traffic safety.
Highlights
During driving, drivers usually receive more than 80% of traffic environment information through their vision [1,2]
This paper will use the least squares of change-point estimation to detect and analyze eye movement changes of female drivers in anxiety
The change-point method is a powerful tool to detect whether any changes have occurred [30,31,32,33,34]. It can determine the number of changes and estimate the time and location of each change. It is capable of detecting small changes, and is preferable when dealing with large data sets
Summary
Drivers usually receive more than 80% of traffic environment information through their vision [1,2]. Hills et al [20,21] found that driver’s eye movements change with the level of driving experience. It is not easy for novice drivers to apply appropriate eye-movement patterns to match the hazardousness of the road. The findings of the study were used to provide effective strategies to improve road safety He et al [11] analyzed the effects of highway tunnel lighting environment on driving safety, using drivers’ eye movement parameters. Most research on drivers’ eye movements has focused on exploring drivers’ eye movement characteristics, as well as the relationship between visual search mode and driving emotion. This paper will use the least squares of change-point estimation to detect and analyze eye movement changes of female drivers in anxiety
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