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Objectives: Drowsiness has been shown to impair reaction times, decision-making abilities, and awareness of hazards critical to driving in a manner similar to the effects of driving under the influence of alcohol.1 Drowsy driving has been estimated to cause thousands of fatal crashes per year and has resulted in billions of dollars in lost revenue.2 However, while breathalyzers and ignition interlock devices can be used to mitigate the risk of drunk driving, no such devices are commercially available for roadside or behind-the-wheel diagnostics for drowsiness. To address this gap, the objective of this study was to demonstrate the efficacy and feasibility of a breath sensor to detect fatigue and drowsiness in a simulated driving environment. Data and methods: Solid state breath sensors calibrated to previously identified breath biomarkers of fatigue and drowsiness were arrayed to determine changes in the breath profiles of subjects undergoing a full-motion driving simulation designed to elicit driver fatigue. A multi-sensor approach was used rather than relying on a single biomarker. The biomarker profile targeted compounds such as benzene derivatives (e.g., benzene, benzaldehyde, toluene, phenol), 2-butanone, and 2-ethyl-1-hexanol, among others. To address potential confounders such as eating/drinking, cigarette smoke, alcohol, and medical conditions like COPD or diabetes, the study was conducted under controlled conditions: participants refrained from eating for a couple of hours before the experiment, were nonsmokers, had not consumed alcohol for the past day, and had no known medical conditions. The breath biomarker profiles were then correlated with established drowsiness metrics, specifically the PERCLOS (Percentage of Eye Closure) and the Karolinska Sleepiness Scale (KSS). Results: The breath sensor array collected breath biomarker profiles for 61 subjects. The biomarker profiles demonstrated a trend consistent with increasing PERCLOS and KSS responses (see Figure). During the 60-minute driving simulation designed to elicit driver fatigue, the PERCLOS showed a steady increase in response over time, while the KSS did not show an increase in perceived sleepiness until the 60 min mark. Similarly, the breath biomarker sensor response showed a significant increase at the 60-minute timepoint, consistent with increases in both the PERCLOS and the KSS, demonstrating evidence of increased driver fatigue and drowsiness. Conclusions: These findings suggest that integrating drowsiness breath sensor arrays into vehicle systems could significantly enhance drowsiness detection, thereby reducing crashes, injuries, and fatalities. Moreover, the scalability and non-invasiveness of the proposed system lays the groundwork for broader applications, extending beyond vehicles to fields such as aviation and healthcare. [ABSTRACT FROM AUTHOR]