Sleep loss and change detection in simulated driving

Abstract

Driver sleepiness is a leading contributor to road crashes. Sleep-related crashes are more likely to involve collision with a stationary object than non-sleep-related crashes. The mechanism underpinning this is unknown; one potential explanation may be an increased propensity for change blindness. Twenty-four drivers with at least one year of independent driving experience completed two simulated drives: one following a normal night of sleep (7-8h) and one following sleep restriction (5h). The drive consisted of 5 laps of an 11.3 km circuit, taking approximately 45 min. Each lap comprised half urban and half rural driving environments.Twenty times during the drive the visual screen was blanked for 500 ms, and when it reappeared participants were asked whether there were any changes. Twelve times a change occurred, and eight times no change occurred. Additionally, four unexpected changes occurred; for example, the language of the road signs was changed from English to German. At the end of each drive, participants were asked if anything unusual occurred. Sleep loss resulted in significantly increased subjective sleepiness and subjective workload. Driving in an urban environment did not increase alertness; subjective sleepiness ratings did not significantly differ between urban and rural environments. Change detection accuracy for both cued and unexpected changes was not significantly affected by sleep loss. In line with previous research, accuracy was greater for changes with high safety relevance and those occurring in rural environments. Collectively the findings of the study suggest that increase change blindness is probably not a contributor to sleep-related road crashes; however, future on-road research and with greater levels of sleep loss is needed to confirm findings.

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving

“…The current study extends the application of CAMs to visualize the efficacy of a deep learning approach to the application of classifying driver states (e.g., inactive, sleepy). The CAM approach presented in this study provides a new way to visualize the previously researched physiological and behavioural impacts of sleep restriction (e.g., slower reaction time) on drivers [4,8,23,52,53]. The rapid and inconsistent movements displayed in the 5-h sleep history class by both networks may be indicative of the slower reaction times experienced by drivers who are sleep restricted [8,15,54,55].…”

Change blindness in simulated driving in individuals with homonymous visual field loss

“…Finding change blindness in the seeing hemifield in both vision groups corroborates previous research investigating change blindness in NV participants in driving situations using flicker paradigms (Beanland et al, 2017 ; Caird et al, 2005 ; Galpin et al, 2009 ; Koustanaï et al, 2012 ; McCarley et al, 2001 , 2004 ), while watching videos (Martens, 2011), and while driving in the simulator (Harms & Brookhuis, 2016 ; Charlton & Starkey, 2013 ; Zheng et al, 2010; Velichkovsky et al, 2002 ; Lee et al, 2007 ; Filtness, et al, 2020 ). Importantly, we demonstrated change blindness to a potential hazard within a traffic scene without requiring an artificial visual disruption (see also Velichkovsky et al, 2002 ).…”

“…However, those paradigms do not address failures of awareness that may happen within a driving scene, such as when a pedestrian suddenly appears from behind a car. Many studies have used a flicker paradigm with inserted blanks, while participants drove in a simulator (Velichkovsky et al, 2002 ; Lee et al, 2007 ; Filtness et al, 2020 ; White & Caird, 2010 ). The artificial blanks represented the type of global disruptions caused by eye movements or a blink (Simons, 1996 ).…”

“…This approach is limited in ecological validity because the blanks are simulations of the visual disruptions, which might or might not have occurred at the time of an eye movement. In addition, eye movements have not always been tracked, so it is not always known whether all the conditions for change blindness were met (e.g., Lee et al, 2007 ; Filtness et al, 2020 ), and if eye tracking was recorded, it can be rare for a change to occur during an eye movement serendipitously (Velichkovsky et al, 2002 ).…”

24th international symposium on shiftwork and working time: innovations in research and practice improving shiftworker health & safety

“…Sleepiness is a risk factor for driving accidents, although not all such accidents are limited to falling asleep at the wheel (Åkerstedt 2000;Beanland et al 2013;Tefft 2010). During a simulated driving task, Filtness et al investigated whether change blindness may contribute to fatigue-related driving accidents (Filtness et al 2020). A change detection task was administered during a 45-min simulated drive following one normal night of sleep (7-8 h) and following one night of sleep restriction (5 h) in a counterbalanced order.…”