Next-Generation Driver Monitoring Systems: A Review of Multimodal Detection of Distraction, Drowsiness, and Sudden Medical Conditions (Statistics, Optimization & Information Computing, 2026)
Driver impairment caused by distraction, drowsiness, and sudden medical conditions remains a major contributor to road traffic accidents worldwide. This paper presents   a comprehensive review of recent advances in real-time detection systems targeting these three domains, with the goal of informing the development of effective and deployable driver monitoring solutions. Literature on distraction detection reveals a transition from traditional gaze-based thresholds to efficient deep learning architectures deployable on embedded platforms, including privacy-preserving approaches using vehicle dynamics alone. Drowsiness detection re- search has progressed toward multimodal and personalized frameworks that integrate physiological, behavioral, and contextual indicators, enhancing detection accuracy and reducing false alarms. Research on sudden medical condition detection, although less extensive, demonstrates promising results through wearable biosensors, in-cabin sensing technologies, and automation fallback strategies capable of executing controlled vehicle stops during acute health events. Across these domains, the review identifies persistent challenges: limited availability of large-scale, diverse, and naturalistic datasets; reliance on controlled laboratory or simulator environments; and the need for adaptive, context- aware, and computationally efficient algorithms suitable for real-world deployment. The integration of multimodal sensing, personalized detection thresholds, and uncertainty estimation is highlighted as a promising direction for improving robustness and user acceptance. By synthesizing findings across diverse methodologies and application contexts, this review provides a consolidated understanding of the state of the art, identifies critical research gaps, and outlines pathways toward next-generation driver monitoring systems capable of reducing crash risk and enhancing road safety.