Abstract
Driving is a safety-critical task that requires an alert and vigilant driver. Most research on the topic of vigilance has focused on its proximate causes, namely low arousal and resource expenditure. The present article aims to build upon previous work by discussing the ultimate causes, or the processes that tend to precede low arousal and resource expenditure. The authors review different aspects of fatigue that contribute to a loss of vigilance and how they tend to occur; specifically, the neurochemistry of passive fatigue, the electrophysiology of active fatigue, and the chronobiology of sleep-related fatigue.
Highlights
According to the Centers for Disease Control Prevention (2020), motor vehicle crashes are the second leading cause of accidental or unintentional death in the United States
The authors want to quickly note that driving using automation will be the primary example used throughout the present paper, the underlying mechanisms discussed below may be applicable to other tasks and domains that involve vigilance as well, such as radar operators, anesthesia monitors, air traffic controllers, and cockpit pilots (Donald, 2008; Wiggins, 2011)
The role of the locus coeruleus and norepinephrine (LC-NE) system will be discussed as it relates to arousal: the way in which it modulates its firing rate to either broaden or narrow our attentional filter, how it recruits different brain regions to assess the costs and benefits of performing a goal-directed task, and how it works with other brain networks to facilitate exploitative or explorative behavior
Summary
According to the Centers for Disease Control Prevention (2020), motor vehicle crashes are the second leading cause of accidental or unintentional death in the United States. Driving automation systems (DASs; e.g., adaptive cruise control and active lane keeping) have been introduced to mitigate these crash rates by reducing workload on the driver (Wickens et al, 2010; Wickens, 2018) They may, inadvertently introduce new problems to the driver (Mueller et al, 2021), such as increasing the prevalence of driver distraction (Young, 2012; Greenlee et al, 2018), drowsiness (Gimeno et al, 2006; Gaspar et al, 2017; Sikander and Anwar, 2018; Kundinger et al, 2020), engagement in non-driving related tasks (Seppelt and Victor, 2016; Cabrall et al, 2019; Mueller et al, 2021), and loss of situational awareness (Berberian et al, 2017; Brandenburg and Chuang, 2019; Lohani et al, 2019). The authors want to quickly note that driving using automation will be the primary example used throughout the present paper, the underlying mechanisms discussed below may be applicable to other tasks and domains that involve vigilance as well, such as radar operators, anesthesia monitors, air traffic controllers, and cockpit pilots (Donald, 2008; Wiggins, 2011)
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