Continuous monitoring of visual distraction and drowsiness in shift-workers during naturalistic driving

• Published in: Safety science Vol. 119; pp. 112 - 116
• Main Authors: Kuo, Jonny, Lenné, Michael G., Mulhall, Megan, Sletten, Tracey, Anderson, Clare, Howard, Mark, Rajaratnam, Shantha, Magee, Michelle, Collins, Allison
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.11.2019; Elsevier BV
• Subjects: Automobile drivers; Automobile driving; Distraction; Driver behaviour; Driver engagement; Driver fatigue; Driver monitoring; Drivers; Driving conditions; Drowsiness; Monitoring; Monitoring systems; Naturalistic driving; Public health; Real time; Roads & highways; Traffic accidents & safety; Visual signals; Visual time-sharing; Naturalistic driving; Distraction; Driver behaviour; Driver engagement; Visual time-sharing; Driver monitoring
• DOI: 10.1016/j.ssci.2018.11.007
• ISSN: 0925-7535

•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. 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.