Continuous monitoring of visual distraction and drowsiness in shift-workers during naturalistic driving. (November 2019)

• Record Type: Journal Article
• Authors: Kuo, Jonny; Lenné, Michael G.; Mulhall, Megan; Sletten, Tracey; Anderson, Clare; Howard, Mark; Rajaratnam, Shantha; Magee, Michelle; Collins, Allison
• Is Part Of: Safety science. Volume 119(2019)
• Page Start: 112
• Page End: 116
• Publication Date: 2019-11
• Subjects: Distraction -- Driver behaviour -- Driver engagement -- Driver monitoring -- Naturalistic driving -- Visual time-sharing; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2018.11.007
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: Drowsiness and visual distraction examined in NDS of shift-workers. Achieved continuous monitoring of real-world distracted driving behavioural signals. Drivers significantly more likely to look toward their lap when drowsy. Lap visual time-sharing sequences less frequent but longer duration on drowsy trips. Abstract: Driver drowsiness is a significant public health problem and has previously been linked to an increase in drivers' propensity to engage in visual distraction. This relationship however has yet to be examined under naturalistic driving conditions, where task demands may differ from lab-based experimental studies. This study aimed to examine the behavioural and physiological signals associated with visual distraction in real-world driving through a world-first application of a real-time driver monitoring system. Using a continuous driver monitoring system, shift-workers (N = 20) were observed on their commutes to and from work. Classifying off-road glances into glances to the driver lap and centre console regions of the vehicle revealed differences in the propensity for drivers to look away from the forward roadway toward these regions, with drivers significantly more likely to look toward their lap when drowsy. These glances were subsequently clustered and analysed in the context of visual time-sharing sequences. Our findings carry impact not only within the subjects of drowsiness and distraction but are also broadly applicable in the context of naturalistic driving methodology where real-time assessment of driver state can facilitate the analysis of large naturalistic datasets.