Change-Point Analysis of Eye Movement Characteristics for Female Drivers in Anxiety

Yongqing Guo, Xiaoyuan Wang, Qing Xu, Feifei Liu, Yaqi Liu and Yuanyuan Xia
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Yongqing Guo: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Xiaoyuan Wang: College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
Qing Xu: Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
Feifei Liu: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Yaqi Liu: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Yuanyuan Xia: College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China

IJERPH, 2019, vol. 16, issue 7, 1-17

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.

Keywords: driving anxiety; eye movement; change-point analysis; least squares method (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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