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Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

Engineering and Technology

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

K. Koch, M. Maritsch, et al.

This groundbreaking research by Kevin Koch, Martin Maritsch, Eva van Weenen, Stefan Feuerriegel, Matthias Pfäffli, Elgar Fleisch, Wolfgang Weinmann, and Felix Wortmann showcases an innovative machine learning system for predicting critical blood alcohol concentration levels using driver monitoring cameras. With impressive accuracy in detecting intoxication, this study could revolutionize drunk driving detection and prevention.

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Playback language: English
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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