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Abstract
This paper develops and evaluates an in-vehicle machine learning system to predict critical blood alcohol concentration (BAC) levels using driver monitoring cameras. A simulator study with 30 participants showed the system reliably detects driving under any alcohol influence (AUROC 0.88) and above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). The model relies on pathophysiological effects of alcohol consumption, demonstrating the potential of driver monitoring cameras for drunk driving detection and prevention.
Publisher
Preprint
Published On
Jan 01, 2023
Authors
Kevin Koch, Martin Maritsch, Eva van Weenen, Stefan Feuerriegel, Matthias Pfäffli, Elgar Fleisch, Wolfgang Weinmann, Felix Wortmann
Tags
machine learning
blood alcohol concentration
driver monitoring
drunk driving
simulation study
alcohol detection
automotive safety

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