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

May, Jennifer F.; Baldwin, Carryl L.

doi:10.1016/j.trf.2008.11.005

Cited by 341 publications

(244 citation statements)

References 25 publications

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“…Thus, during simple and automated driving, as in this study, negative effects of sleepiness on driving performance will be difficult to observe by the drivers themselves and the difference in the objective sleepiness indicators at high level of self-reported sleepiness might be due to the drivers not understanding their own signs of sleepiness. The results received in this pilot study also support the statement that countermeasures for driver fatigue need to consider the reason behind [30]. .…”

Section: Discussionsupporting

confidence: 79%

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The severity of driver fatigue in terms of line crossing: a pilot study comparing day- and night time driving in simulator

Anund

Fors

Ahlström

2017

Eur. Transp. Res. Rev.

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Introduction The overall aim of this study is to compare daytime driving with night-time driving looking at line crossings during self-reported sleepiness and long blinks. The hypothesis is that high levels of self-reported sleepiness (KSS 9) and long blink duration (>0.15 s) will be less associated with critical events during the day-time compared to night-time. Method The study is based on data from a driving simulator experiment with 16 participants driving 150 km on a typical Swedish motorway scenario twice: once during daytime and once during night time. In total data from 6 segments of 4 km each equally distributed along the drive was averaged and included in the analysis. A Mixed Model Anova was used to test the effects on KSS, Blink Duration and Line Crossings with factors for Session (Day/Night) and Road segment (1-6), and participant as random. In addition, a logistic regression was used to identify when there is a risk for line crossings. Finally, the proportion of line crossings in relation to high KSS values and long blink durations was tested with Fisher's exact test. Results The results show no differences in the percentage of Line Crossings to the left during high levels of Karolinska Sleepiness Scale during daytime (33%) compare to night-time (40%). However, there was a significant difference between day and night time line crossings while the driver had long duration blinks (4% during daytime and 35% during night-time). Despite these results the most promising predictor of line crossings in each segment of 4 km/h was KSS with an Odds Ratio of 5.4 with a reference value at Karolinska Sleepiness Scale level 5. Conclusion In conclusion, the results do not support the hypothesis that high levels of KSS will result in more frequent line crossings at night time compared to day time. However, the result supports the hypothesis that long blink durations are associated with more line crossings when they appear during night time than during daytime.

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Section: Discussionsupporting

confidence: 79%

“…The terms sleepiness and fatigue are often used synonymously even though the causal factors contributing to the state may differ [14]. The main determinants of sleepiness are the time of day (circadian rhythm) and the duration of previous periods of being awake and asleep (homeostatic regulation) [15,16].…”

Section: Introductionmentioning

confidence: 99%

The severity of driver fatigue in terms of line crossing: a pilot study comparing day- and night time driving in simulator

Anund

Fors

Ahlström

2017

Eur. Transp. Res. Rev.

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“…These include driverrelated physiological measures, such as ocular measures, including blink duration and slow eyelid closures (Wierwille & Ellsworth, 1994;Åkerstedt, Peters, Anund, & Keckland, 2005;Anund et al, 2008;Åkerstedt et al, 2010) and electroencephalography (EEG) algorithms (Cajochen et al, 1999;Horne & Reyner, 1996), or vehicle-related measures, such as lane departure measures (May & Baldwin, 2009;Mortazavi, Eskandarian, & Sayed, 2009). Eyelid closure appears to be a promising method for indicating driver drowsiness in real-time (Dinges, Mallis, Maislan, & Walker-Powell, 1999;Ftouni et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Jackson

Raj

Croft

et al. 2016

Traffic Injury Prevention

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(2016). Slow eyelid closure as a measure of driver drowsiness and its relationship to performance. Traffic Injury Prevention, 17 (3), 251-257. Slow eyelid closure as a measure of driver drowsiness and its relationship to performance AbstractObjective: Slow eyelid closure is recognized as an indicator of sleepiness in sleep-deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure are evident following acute sleep deprivation as assessed by an automated device and how ocular parameters relate to performance after sleep deprivation. Methods: Twelve healthy professional drivers (45.58 ± 10.93 years) completed 2 randomized sessions: After a normal night of sleep and after 24 h of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task. Results: Following sleep deprivation, drivers displayed significantly more eyelid closure (P < .05), greater variation in lane position (P < .01) and more attentional lapses (P < .05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r = 0.68, P < .05) and variation in lane position on the driving task (r = 0.61, P < .05). Conclusions: Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.

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“…Both systems can intervene in the driving process, to avoid hazardous situations for the driver. Other ADAS systems monitor drivers fatigue by analyzing its facial features (eyes), the inclination of its head or the characteristic of its voice [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

Machot

Mosa

Fasih

et al. 2012

Autonomous Systems: Developments and Trends

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This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.

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

Cited by 341 publications

References 25 publications

The severity of driver fatigue in terms of line crossing: a pilot study comparing day- and night time driving in simulator

The severity of driver fatigue in terms of line crossing: a pilot study comparing day- and night time driving in simulator

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

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