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

Barr, Lawrence C; Yang, Chao; Hanowski, Richard J.; Olson, Rebecca L

doi:10.3141/1937-08

Cited by 18 publications

(10 citation statements)

References 3 publications

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“…Younger and less experienced drivers are generally associated with higher risk of drowsy driving (Barr et al, 2005;Häkkänen & Summala, 2001;Smith et al, 2009). According to Duke et al (2010), the drowsiness symptoms were minimum among the middle-aged drivers compared to the young and elderly drivers.…”

Section: Driver Demographicsmentioning

confidence: 98%

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Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Mahajan

Velaga

Kumar

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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The aim of the study is to identify and model the role of payment incentives, driver work-rest patterns and other lifestyle habits influencing the drowsy driving behavior among long-haul truck drivers. To achieve this aim, this study targeted two main objectives: (1) to examine the significant differences between the groups of drowsy and non-drowsy drivers based on the opportunities of monetary incentives and (2) to examine the role of different factors: driver demographics, work-rest patterns, lifestyle and occupational characteristics particularly incentives associated with driving in causing driver sleepiness among Indian truck drivers. The study is based on interview responses from 453 long-haul truck drivers approached in three Indian cities-Mumbai, Indore and Nagpur. Initial principal component analysis of the responses related to financial incentives (occupational characteristics) resulted into two correlated factors: (i) willingness to earn extra payments if offered (WEP) and (ii) incentives available in the current driving experience (ICD) that influence driver work-rest patterns and alertness while driving. Kruskal-Wallis test showed a significant difference among the groups of sleepy and non-sleepy drivers due to these factors (WEP and ICD). Finally, a logistic regression model showed that long driving duration, working days per week, rest patterns, insufficient sleeping hours and history of violations were found significantly associated with drowsy driving among the long-haul truck drivers. Increase in consumption of caffeine and tobacco indicated reduction in driver alertness. According to the model results, the odds of drowsy driving were 77% less for drivers between 46-55 years compared to the young drivers (<25 years). Driving under the influence of financial incentives was observed to increase the odds of falling asleep by 1.58 times among the truck drivers. This was apparently the most interesting and intriguing result of the study indicating the need for further research on the influence of financial or socioeconomic motivations to sleepiness.

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Section: Driver Demographicsmentioning

confidence: 98%

“…Hypothesis 5: Accepting opportunities for earning incentives can increase the risks of drowsy driving among truck drivers. Barr et al, 2005;Häkkänen & Summala, 2001;Duke et al, 2010;Otmani, Roge, & Muzet, 2005 Risk of drowsy driving shows a u-shaped curve with age.…”

Section: Incentives and Work-rest Patternmentioning

confidence: 99%

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Mahajan

Velaga

Kumar

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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show abstract

“…There are numerous valuable studies on the effect of driver's inattention and drowsiness on driving performance for truck driver using field data [15], [16]. Because of the risks of involving the drivers in dangerous drowsy scenarios, some researchers tend to perform the experiments in a simulated environment [17]- [21].…”

mentioning

confidence: 99%

Evaluation of a Smart Algorithm for Commercial Vehicle Driver Drowsiness Detection

Eskandarian

Mortazavi

2007

2007 IEEE Intelligent Vehicles Symposium

110

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Drowsiness is a safety hazard in commercial vehicle driving. The conditions to which truck drivers are exposed put them at higher risk as compared to passenger car drivers. Unobtrusive drowsiness detection methods can avoid catastrophic crashes by warning or assisting the drivers. This paper describes an experimental analysis of commercially licensed drivers who were subjected to drowsiness conditions in a truck driving simulator and evaluates the performance of a neural network based algorithm which monitors only the drivers' steering input. Correlations are found between the change in steering and the state of drowsiness. The results show steering signals differences can be used effectively for detection.

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“…Unintentional lane deviations involve drifting outside of the driving lane. In addition, naturalistic observation studies have suggested a possible link between fatigued and distracted driving in CMV drivers (Barr, Yang, Hanowski, & Olson, 2005). Of the safety-critical events identified in the data, 60 percent had some type of tertiary task (e.g., non-driving related tasks such as eating) listed as a potential contributing factor (Olson, Hanowski, Hickman, & Bocanegra, 2009).…”

Section: Dr Iver Distr Actionmentioning

confidence: 98%

Synthesis of Literature Relating to Cellular Telephone/Personal Digital Assistant Use in Commercial Truck and Bus Operations

Morgan¹,

Trimble²,

Bowman³

et al. 2011

PsycEXTRA Dataset

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

Cited by 18 publications

References 3 publications

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Evaluation of a Smart Algorithm for Commercial Vehicle Driver Drowsiness Detection

Synthesis of Literature Relating to Cellular Telephone/Personal Digital Assistant Use in Commercial Truck and Bus Operations

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