Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China; State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China.
Ipsos, User Experience, Chicago, IL 60601, USA; School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100191, China.
J Safety Res. 2019 Dec;71:219-229. doi: 10.1016/j.jsr.2019.09.012. Epub 2019 Nov 20.
Intersections are the most dangerous locations in urban traffic. The present study aims to investigate drivers' visual scanning behavior at signalized and unsignalized intersections.
Naturalistic driving data at 318 green phase signalized intersections and 300 unsignalized ones were collected. Drivers' glance allocations were manually categorized into 10 areas of interest (AOIs), based on which three feature subsets were extracted including glance allocation frequencies, durations and AOI transition probabilities. The extracted features at signalized and unsignalized intersections were compared. Features with statistical significances were integrated to characterize drivers' scanning patterns using the hierarchical clustering method. Andrews Curve was adopted to visually illustrate the clustering results of high-dimensional data.
Results showed that drivers going straight across signalized intersections had more often glances at the left view mirror and longer fixation on the near left area. When turning left, drivers near signalized intersections had more frequent glances at the left view mirror, fixated much longer on the forward and rearview mirror area, and had higher transition probabilities from near left to far left. Compared with drivers' scanning patterns in left turning maneuver at signalized intersections, drivers with higher situation awareness levels would divide more attention to the forward and right areas than at unsignalized intersections.
This study revealed that intersection types made differences on drivers' scanning behavior. Practical applications: These findings suggest that future applications in advanced driver assistance systems and driver training programs should recommend different scanning strategies to drivers at different types of intersections.
交叉口是城市交通中最危险的地方。本研究旨在调查驾驶员在信号交叉口和无信号交叉口的视觉扫视行为。
在 318 个绿色相位信号交叉口和 300 个无信号交叉口收集了自然驾驶数据。根据驾驶员的视线分配情况,将其手动分为 10 个感兴趣区域(AOIs),基于此提取了三个特征子集,包括注视分配频率、持续时间和 AOI 转移概率。比较了信号交叉口和无信号交叉口的提取特征。将具有统计学意义的特征集成到使用层次聚类方法来描述驾驶员扫描模式的特征中。采用安德鲁斯曲线直观地说明高维数据的聚类结果。
结果表明,直通过信号交叉口的驾驶员更频繁地看左后视镜,对近左区域的注视时间更长。当左转时,靠近信号交叉口的驾驶员更频繁地看左后视镜,对前视镜和后视镜区域的注视时间更长,从近左到远左的转移概率更高。与信号交叉口左转操作时驾驶员的扫视模式相比,具有更高情境意识水平的驾驶员会将更多注意力分配到前视镜和右侧区域,而不是在无信号交叉口。
本研究表明交叉口类型对驾驶员的扫视行为有影响。实际应用:这些发现表明,在先进的驾驶员辅助系统和驾驶员培训计划中的未来应用应该向不同类型交叉口的驾驶员推荐不同的扫视策略。