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Analyzing Driver Drowsiness: From Causes to Effects

Author

Listed:
  • Sónia Soares

    (Faculty of Engineering of the University of Porto, Research Centre for Territory, Transports and Environment, 4200-465 Porto, Portugal)

  • Tiago Monteiro

    (Faculty of Psychology and Educational Sciences of the University of Porto, Center for Psychology at University of Porto, 4200-135 Porto, Portugal)

  • António Lobo

    (Faculty of Engineering of the University of Porto, Research Centre for Territory, Transports and Environment, 4200-465 Porto, Portugal)

  • António Couto

    (Faculty of Engineering of the University of Porto, Research Centre for Territory, Transports and Environment, 4200-465 Porto, Portugal)

  • Liliana Cunha

    (Faculty of Psychology and Educational Sciences of the University of Porto, Center for Psychology at University of Porto, 4200-135 Porto, Portugal)

  • Sara Ferreira

    (Faculty of Engineering of the University of Porto, Research Centre for Territory, Transports and Environment, 4200-465 Porto, Portugal)

Abstract

Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.

Suggested Citation

  • Sónia Soares & Tiago Monteiro & António Lobo & António Couto & Liliana Cunha & Sara Ferreira, 2020. "Analyzing Driver Drowsiness: From Causes to Effects," Sustainability, MDPI, vol. 12(5), pages 1-12, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1971-:d:328542
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    Citations

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    Cited by:

    1. Qian Cheng & Xiaobei Jiang & Haodong Zhang & Wuhong Wang & Chunwen Sun, 2020. "Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    2. Sónia Soares & Carlos Campos & João Miguel Leitão & António Lobo & António Couto & Sara Ferreira, 2021. "Distractive Tasks and the Influence of Driver Attributes," Sustainability, MDPI, vol. 13(9), pages 1-20, May.
    3. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Ahmed Farouk Kineber & Muhammad Ajmal, 2021. "Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach," Sustainability, MDPI, vol. 13(16), pages 1-17, August.

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