ScholarWorks@성균관대학교

초록

Driver drowsiness poses a significant threat to road safety, contributing to numerous accidents globally, according to historical statistical data. This study provides an exhaustive overview of driver drowsiness, encompassing its symptoms, causes, prevention strategies, and underlying physiological and neural changes that occur when transitioning from wakefulness to a drowsy state. This review paper explores the complexities of detecting driver drowsiness, with a focus on brain imaging-based methodologies, and addresses four key research questions. We systematically analyze and review existing research on driver drowsiness detection using machine learning algorithms at both macro and micro levels, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We categorize brain imaging modalities into four main groups: electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging, and magnetoencephalography. Micro analysis (from 2020 to 2025) explores the application of these modalities in detecting driver drowsiness, discussing their strengths and limitations, and evaluating their effectiveness in simulated and real-world driving experiments. Our evaluation reveals that EEG and fNIRS have emerged as the most prevalent brain imaging methodologies for detecting driver drowsiness, due to their non-invasive and portable nature, high temporal and spatial resolution, and real-time capability. However, challenges persist, including the need for more robust machine learning algorithms, improved signal processing techniques, and enhanced sensor technologies. Furthermore, there is a need for more comprehensive studies that integrate multiple brain imaging modalities and machine learning approaches to detect driver drowsiness. By addressing these key areas, future research can advance the field of driver drowsiness detection and pave the way for safer and more efficient transportation systems.

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