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Drowsy driving contributes to 10–30% of all vehicle crashes, making it a major road safety concern. Driver Monitoring Systems (DMS) aim to assess driver alertness and are typically categorized into vehicle-, behavior-, and physiology-based approaches. While physiology-based systems offer the highest accuracy, most of them rely on costly and intrusive contact-based cardiac sensors. This study demonstrates, for the first time, the feasibility of predicting driver sleep events using a fully contactless, physiology-based approach that analyzes breathing patterns in real time. Data were collected using a short-range 60 GHz standalone automotive radar in a driving-seat mockup. Crucially, sleep events were objectively validated for the first time in this context using polysomnography (PSG) data reviewed by a medical expert, following American Academy of Sleep Medicine (AASM) guidelines for the Maintenance of Wakefulness Test (MWT) — marking a departure from previous reliance on subjective behavioral observations. The proposed heuristic algorithm achieved 85% overall accuracy, with 100% (95% CI 29%–100%) specificity and 80% (95% CI 44%–97%) sensitivity. This work presents a validated, non-intrusive solution for sleep event prediction in drivers, underscoring its potential for enhancing road safety through practical, clinically supported DMS technologies.