Preliminary investigation of sleep-related driving fatigue experiment in Indonesia

Sanjaya, Kadek Heri; Kusuma Sya'Bana, Yukhi Mustaqim; Hutchinson, Shaun; Diels, Cyriel · 2018 · Crossref

DOI: 10.14203/j.mev.2018.v9.8-16

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Summary

This paper addresses the lack of experiment-based driving fatigue studies in Indonesia, where sleep-related fatigue is a significant cause of traffic accidents. The authors aimed to develop a laboratory-based experimental procedure for measuring sleep-related driving fatigue. To achieve this, they conducted a literature review to establish methodological standards and performed three pilot experiments to test instruments and procedures. The study utilized a driving simulator running OpenDS software and LCT Sim, equipped with modified game controllers to simulate realistic driving conditions. Three subjects, comprising both experienced and inexperienced drivers, participated in the trials. The experiments measured driving performance through brake and lane change reaction times and assessed subjective sleepiness using the Karolinska Sleepiness Scale (KSS). The experimental conditions varied by illumination intensity (high vs. low) and timing relative to lunch (pre- vs. post-lunch) to induce drowsiness. The pilot experiments revealed that lane change reaction time exhibited greater fluctuation than brake reaction time, suggesting it is a more sensitive metric for detecting driving performance degradation. The data indicated that post-lunch sessions induced higher levels of drowsiness compared to variations in illumination intensity. Furthermore, the relationship between KSS scores and driving performance data appeared non-linear, with sleepiness levels peaking in the middle of sessions rather than at the end, likely due to increased subject motivation near the conclusion of the trials. The study also noted that driving experience influenced reaction times, with inexperienced drivers showing different patterns in lane change responses. Although an eye tracker was tested, it was not used for quantitative analysis in this preliminary phase due to calibration challenges and cost. The authors conclude that while the proposed methods show potential, the small sample size limits statistical significance and generalizability. They recommend future studies involve a larger number of subjects, ideally exceeding sixteen for statistical validity, and include physiological measurements such as EEG or eye closure detection for more objective fatigue assessment. The study highlights the need to further investigate the effects of circadian rhythm, sleep deprivation, and meal timing on driving fatigue. Additionally, the authors suggest refining the experimental design by withholding information about trial duration to prevent motivational artifacts and considering the use of peripheral detection tasks to complement lane change tests. This preliminary work lays the groundwork for more rigorous investigations into sleep-related driving fatigue in the Indonesian context.

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Topics

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• drowsiness detection algorithms
• drowsiness
• sleep deprivation
• time on task
• truck driver fatigue
• vigilance

Information type

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• Empirical Findings: physiological data, behavioral performance data
• Methodological Resource: tool software