School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China.
College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China.
Int J Environ Res Public Health. 2019 Apr 7;16(7):1236. doi: 10.3390/ijerph16071236.
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.
驾驶员危险感知能力与交通事故的发生密切相关，而视觉是我们感知风险最重要的感觉器官。因此，探索驾驶员眼动特征对于促进道路安全具有重要意义。本研究重点分析了驾驶员在焦虑状态下眼动特征的变化。我们使用了各种材料来诱导驾驶员产生焦虑，然后进行了真实驾驶实验和驾驶模拟，以收集驾驶员的眼动数据。然后，我们比较了驾驶员在平静和焦虑状态下的眼动特征差异，以提取关键的眼动特征。采用最小二乘法的变点分析方法来检测眼动特征的突然变化的时间和位置。结果表明，最小二乘法可有效识别女性驾驶员在焦虑状态下的眼动变化。研究还发现，道路环境的变化会导致女性驾驶员的注视次数和注视持续时间显著增加，例如在交通事故或急转弯场景中。本研究的结果可用于识别道路环境中的意外事件，并改善弯道的几何设计。本研究还可用于开发车辆中的主动驾驶预警系统和智能人机交互。本研究对于提高道路交通安全具有重要的理论意义和应用价值。
