Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving. Issue 6 (25th May 2020)

• Record Type: Journal Article
• Authors: Niu, Jianwei; Xu, Haixin; Sun, Yipin; Qin, Hua
• Is Part Of: Human factors and ergonomics in manufacturing and service industries. Volume 30:Issue 6(2020)
• Page Start: 377
• Page End: 384
• Publication Date: 2020-05-25
• Subjects: automated driving -- dynamic video -- hazard level -- take‐over -- visual perception characteristics; Computer integrated manufacturing systems -- Periodicals; Human engineering -- Periodicals; 620.8205
• DOI: 10.1002/hfm.20860
• Languages: English
• ISSNs: 1090-8471

Abstract

Abstract: A high degree of automated driving distracts drivers more easily, resulting in slow recognition of critical events during driving and slow responses to emergencies. Automated driving and manual switching processes are also prone to erroneous decisions. We conducted a simulated automated driving experiment to study participants' visual perception characteristics during the take‐over of vehicle control. The present study used dynamic videos to imitate the driving situations when drivers returned their gaze from the distractive source to the road. We collected the drivers' eye movement data to analyze the search strategy and physiological characteristics of the drivers after the take‐over reminder. The results showed that the instant information search method of the drivers was scanning of the driving scene. When the degree of distraction deepened and the hazard level of scenes increased, the pupil diameter of the drivers increased and the fixation duration became longer. These findings can help to design take‐over warnings and support more intelligent automated driving systems to judge whether measures should be taken to interfere with the driver's operation to avoid collisions. Furthermore, the drivers' fixation point distribution focused on the left side and the lower side of the scene. We suggest that the take‐over warning is displayed in the head‐up display. This study provides a better understanding of drivers' visual perception characteristics when drivers' eyesight returns from other distractors to the driving scene and a good theoretical basis for the design of hazard warning information for automated driving.