Intelligent driver monitoring systems: a survey of drowsiness detection technologies for road safety

Abstract

Driver drowsiness is a recognized contributor to road crashes. This review provides a structured synthesis of driver-monitoring methods spanning image/video, physiological signals (EEG, ECG, PPG, EOG), vehicle behavior, and multimodal fusion. We conducted a systematic search of major bibliographic databases with a PRISMA workflow covering 2020–2025, screened full texts against inclusion criteria, and coded datasets, validation protocols, metrics, and deployment factors (details in Methods). Across modalities, vision has shifted from classical pipelines to deep learning (including Vision Transformers), while physiological approaches offer robustness under poor visibility at the cost of additional hardware and comfort constraints; multimodal fusion improves stability but raises compute and integration burden. Reported performance remains highly protocol-dependent: subject-dependent or within-dataset validation can inflate headline numbers, whereas subject-independent and cross-dataset tests are rarer and typically yield lower—but more realistic—estimates. We highlight and address a benchmarking gap: heterogeneity in datasets and protocols makes cross-paper accuracy comparisons misleading; we therefore standardize reporting and emphasize operational metrics. We recommend prevalence-aware evaluation with precision–recall analysis (PR-AUC), operating-point metrics (e.g., recall at fixed false-alarm rates), calibration assessment, and time-to-detect. We also discuss privacy, regulation, and deployment considerations (edge/on-device processing). Limitations of the evidence base include scarce large-scale on-road studies, inconsistent ground-truthing, and limited cross-demographic validation. We propose a field-specific research agenda and a shared-authority framework in which DMS negotiates control between human and autonomy with verifiable safety metrics. Together, these contributions provide a consolidated map of the field and practical guidance for robust, real-world evaluation.