Assessment of Driver Fatigue, Distraction, and Performance in a Naturalistic Setting

Abstract

The results of a study to characterize episodes of driver fatigue and drowsiness and to assess the impact of driver fatigue on driving performance are documented. This data-mining effort performed additional analyses on data collected in an earlier study by the Federal Motor Carrier Safety Administration of the effects of fatigue on drivers in local and short-haul operations. The primary objectives of the study were to investigate fatigue as a naturally occurring phenomenon by identifying and characterizing episodes of drowsiness during all periods of driving and to determine the operational or driving environment factors associated with drowsy driving. A total of 2,745 drowsy events were identified in approximately 900 total hours of naturalistic driving video data. Higher levels of fatigue were associated with younger and less experienced drivers. In addition, a strong and consistent relationship was found between drowsiness and time of day. Drowsiness was twice as likely to occur between 6:00 a.m. and 9:00 a.m., and approximately 30% of all observed incidences of drowsiness occurred within the first hour of the work shift. Insights about the relationship between driver fatigue and driver distraction and inattention are provided. This study presents an analytical framework for quantitatively assessing driver fatigue and drowsiness as a function of driver characteristics and the driving environment. It is hoped that the results will help to identify effective countermeasures for drowsy driving that will reduce the number of commercial-vehicle-related fatalities and injuries.

Threats to Scientific Validity in Truck Driver Hours-of-Service Studies

“…Neither crashes nor SCEs should be treated as fatigue events without event-specific indications (e.g., from interviews or videos) that fatigue was actually involved. (Blanco et al, 2016) Highest rate in early morning Most likely in heavy urban traffic Most likely on low traffic rural roads Most likely on undivided roads Most likely on divided highways Driver is active, usually distracted (Barr et al, 2011) Driver is passive with tunnel vision and relinquishing vehicle control (Barr et al, 2011). AATW % of CRs = 0.1% (Knipling 2009); 0.5% in Blanco et al, (2016) AATW % of CRs = 3.8% (Knipling 2009) Risk inversely related to PERCLOS (Percent Eye Closure).…”

The Case for Addressing Operator Fatigue

“…Recent laboratory research has shown that distractibility markedly increases when individuals are fatigued (Anderson & Horne, 2006; Anderson, Wales, et al, 2010), and the authors concluded that sleepy individuals may actually seek out distractions, perhaps in an attempt to remain awake and/or due to an inability to suppress their responses to distractions. A video study of long and short-haul truck drivers found that when drowsy, drivers spent less time visually scanning their environment but spent more time adjusting their position, conversing with a passenger, and scratching their face or head (Barr, Yang, Hanowski, & Olson, 2011). The National Transportation Safety Board has found distractions, typically from cell phone usage or text messaging, to be a contributing factor in a series of recent major accidents in all modes of transportation, causing deaths, injuries, and millions in damage (2011a, 2011b, 2011c).…”

Vehicle-based studies of driving in the real world: The hard truth?

“…The selection of drivers can be from a certain group of interest, such as teenage drivers (Lee et al, 2011) or long-haul truck drivers (Barr et al, 2011;Blanco et al, 2011). The participants in the 100 car study were drawn from one geographical region, while in SHRP2 drivers are being recruited from six different regions of the United States.…”