Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies

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Abstract

Driver fatigue is an ill-defined term in the literature. It has been broadly used to refer to a wide range of driver states, each with different causal mechanisms. Technologies currently exist which enable detection of driver fatigue and interventions that have the potential to dramatically reduce crash probability. The successful implementation of these technologies depends on the cause and type of fatigue experienced. Sleep-related (SR) forms of driver fatigue result from accumulated sleep debt, prolonged wakefulness or troughs in the circadian rhythms. SR fatigue is resistant to most intervention strategies. Conversely, technologies for detecting and countering task-related (TR) fatigue (caused by mental overload or underload) are proving to be effective tools for improving transportation safety. Methods of detecting and counteracting the various forms of driver fatigue are discussed. Emphasis is placed on examining the effectiveness of existing and emerging technologies for combating TR forms of driver fatigue.

Introduction

Fatigue is a multidimensional construct that has been difficult for researchers to define (Brown, 1994, Desmond and Hancock, 2001). In fact, fatigue, sleepiness and drowsiness are often used synonymously (Johns, 1998). Fatigue and sleep are contributing factors in thousands of crashes, injuries and fatalities annually (Knipling and Wang, 1994, NHTSA, 2006). Several researchers have reviewed technological devices for the detection and countering of driver fatigue (Boivin, 2000, Hartley et al., 2000, Mallis, 1999). To our knowledge, no attention has been given to differentiating technology by subcategorizing driver fatigue based on the causal factors of the fatigue. This distinction is important when evaluating the effectiveness of these technological devices. Driver fatigue can be subcategorized into sleep-related (SR) and task-related (TR) fatigue based on the causal factors contributing to the fatigued state. Sleep deprivation, extended duration of wakefulness and time of day (circadian rhythm effect) affect SR fatigue. Certain characteristics of driving, like task demand and duration, can produce TR fatigue in the absence of any sleep-related cause.

Certain detection technologies may be helpful in determining both types of fatigue, but technology aimed at increasing alertness may only be suitable for countering TR fatigue. Research has shown that among all the self-initiated strategies that drivers use (i.e. cranking up the AC, blasting the radio, rolling down the window), only naps and caffeine produce a reduction in driver fatigue, specifically in the context of SR fatigue (Horne and Reyner, 2001, Reyner and Horne, 1998, Reyner and Horne, 2000, Reyner and Horne, 2002). This review will focus on examining the effectiveness of driver fatigue technologies in the context of these different causal factors.

SR fatigue can be caused by circadian rhythms, sleep deprivation and sleep restriction. Sleep/wake patterns follow the body’s natural circadian rhythm or internal clock, which drives humans to sleep during the night and be awake during the day. The circadian rhythm also produces an alertness dip in the early afternoon during which people are sleepier (Monk, 1991). Performance decrements are evident during the troughs in the circadian rhythm. For instance, an increased amount of sleep-related crashes occur between 2 and 6:00 am and also between 2 and 4:00 pm, which correspond to these troughs (Pack et al., 1995). Circadian effects have also been demonstrated during a driving simulator task (Lenne, Triggs, & Redman, 1997). Speed deviation (standard deviation of driver’s average speed) varied significantly as a result of time of day, with the greatest variability occurring at 6:00 am, 2:00 pm and 2:00 am. SR fatigue is also influenced by homeostatic factors, such as the duration of wakefulness and sleep deprivation. Performance becomes worse the longer a person remains awake. Sleep restriction, or not obtaining adequate sleep will also result in increased sleepiness and a decline in performance.

In an effort to evaluate the progression of performance decline in response to various levels of sleep deprivation, Jewett, Dijk, Kronauer, and Dinges (1999) tested participants on the psychomotor vigilance task (PVT). The PVT is a reaction time task that requires participants press a button as soon as they see numbers appear on a display. Participants completed this task after 0, 2 (from 3 am to 5 am), 5 (from 1:30 am to 6:30 am) or 8 (from midnight to 8 am) hours of sleep. The PVT was performed at 10 am. All measures of the PVT, including number of lapses, slowest reaction time, fastest reaction time, time on task decrement and median reaction time improved as hours of sleep increased. Results from a 40-h sleep deprivation study showed that PVT performance decreased (lapses increased in frequency and reaction time increased) as the homeostatic pressure to sleep (or time awake) increased (Graw, Krauchi, Knoblauch, Wirz-Justice, & Cajochen, 2004).

TR fatigue is caused by the driving task and driving environment. Desmond and Hancock (2001), as well as Gimeno, Cerezuela, and Montanes (2006) suggest that driver fatigue can be produced by active or passive TR fatigue. Active fatigue is the most common form of TR fatigue that drivers experience (Desmond & Hancock, 2001). Gimeno et al. (2006) relate active fatigue to mental overload (high demand) driving conditions and passive fatigue with underload conditions. Examples of high task demand situations include high density traffic, poor visibility, or the need to complete an auxiliary or secondary task (i.e. searching for an address) in addition to the driving task. Passive fatigue is produced when a driver is mainly monitoring the driving environment over an extended period of time when most or the entire actual driving task is automated. Passive fatigue may occur when the driving task is predictable. Drivers may start to rely on mental schemas of the driving task which results in a reduction in effort exerted on the task (Gimeno et al., 2006). Underload is likely to occur when the roadway is monotonous and there is little traffic.

Most studies of driver fatigue focus on sleep deprivation or circadian rhythm effects, but require drivers to perform driving tasks during monotonous, highway conditions. This confounds the effects of SR and TR fatigue. Regardless, it is clear that driver fatigue does produce performance decrements in driver simulation and on-road driving tasks. Fig. 1 illustrates the three types of fatigue, their causes, consequences and interactions.

Lenne, Triggs, and Redman (1998) conducted a driving simulator study where participants completed a 20 min drive every 3 h between 8:00 am and 8:00 pm after either an 8 h night of sleep or a night of complete sleep deprivation. The driving simulator task also included a secondary reaction time task. Results showed that lane position variability was greater in the sleep deprivation condition and increased across each trial. Like their previous study, sleep deprived drivers drove closer to the centerline of their lane than non-sleep deprived drivers. The standard deviation of speed was greater for sleep deprived drivers, and was worse at 8:00 am and 2:00 pm, showing a circadian effect on performance. Mean reaction time was greater in the sleep deprived condition and improved throughout the day regardless of condition.

Philip et al. (2005) tested the effects of sleep restriction on a real highway. Participants slept either 8.5 h or 2 h in the laboratory, and then drove on a straight highway with light traffic and fair weather in a car outfitted with additional passenger controls. During the on-road portion of the experiment, a researcher accompanied the driver, prepared to take over the driving task if necessary. Driving sessions were 105 min long and occurred approximately 2 h apart with a total of 5 sessions. After each driving session, participants completed subjective sleepiness scales and a 10 min reaction time test. Participants in the sleep restricted condition exhibited significantly more inappropriate line crossings, increasing the risk of such an action by 8 times that of rested drivers. Mean reaction time and subjective sleepiness were both greater for the sleep restricted participants.

Thiffault and Bergeron (2003) tested the effects of monotonous environments on driving performance during a simulated driving task and revealed poorer performance in the monotonous condition, as indicated by a greater number of large steering wheel movements (over corrections). Increased automation of the driving task (e.g., using cruise control) during prolonged driving and the low task demands of monotonous roadway conditions result in passive fatigue (Desmond & Hancock, 2001).

Although the same types of performance decrements may be seen for both SR and TR fatigue, these causal factors must be distinguished when considering technology designed to detect or counter driver fatigue. Devices for detecting driver fatigue may be acceptable for both types of fatigue, but countermeasures will only be effective for combating fatigue. In addition, it is critical to distinguish between active and passive fatigue. Adding an additional task for the driver to perform like a reaction time task, could improve performance during passive fatigue. There are, in fact, commercially available devices that function this way. However, an additional task would be detrimental to a driver experiencing TR active fatigue. The following section addresses driver fatigue technology. Devices are grouped according to those intended for fatigue detection, crash prevention and fatigue reduction. These devices are evaluated for their effectiveness in detecting or countering SR and TR fatigue.

Section snippets

Detection and warning technology

Detection and warning technologies use measures that are sensitive to physiological and performance changes in fatigue, such as eye movements, head-nodding and steering performance. The goal of these devices is to warn the driver of their fatigue so that they can stop driving and rest. Additional research must determine the effectiveness of these devices in the context of different causal factors, such as SR or TR fatigue. However, as SR fatigue may more likely lead to drivers actually falling

Conclusions

Classifying driver fatigue into SR and TR (active and passive) categories based on the causal factors involved will lead to improved development and implementation of fatigue countermeasures. TR active fatigue stemming from high task load driving condition requiring sustained attention and prolonged driving will benefit from increased automation and in-vehicle technologies that offset driver workload. Conversely, further automation of the driving task will exacerbate TR passive fatigue caused

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