Moving Beyond the Simulator: Interaction-Based Drunk Driving Detection in a Real Vehicle Using Driver Monitoring Cameras and Real-Time Vehicle Data

要旨

Alcohol consumption poses a significant public health challenge, presenting serious risks to individual health and contributing to over 700 daily road fatalities worldwide. Digital interventions can play a crucial role in reducing these risks. However, reliable drunk driving detection systems are vital to effectively deliver these interventions. To develop and evaluate such a system, we conducted an interventional study on a test track to collect real vehicle data from 54 participants. Our system reliably identifies non-sober driving with an area under the receiver operating characteristic curve (AUROC) of 0.84 ± 0.11 and driving above the WHO-recommended blood alcohol concentration limit of 0.05 g/dL with an AUROC of 0.80 ± 0.10. Our models rely on well-known physiological drunk driving patterns. To the best of our knowledge, we are the first to (1) rigorously evaluate the potential of (2) driver monitoring cameras and real-time vehicle data for detecting drunk driving in a (3) real vehicle.

受賞
Best Paper
著者
Robin Deuber
ETH Zürich, Zürich, Switzerland
Patrick Langer
ETH Zurich, Zurich, Switzerland
Mathias Kraus
Professor for Explainable AI in Business Value Creation, Regensburg, Bavaria, Germany
Matthias Pfäffli
University of Bern, Bern, Switzerland
Matthias Bantle
Institute of Forensic Medicine, University of Bern , Bern, Switzerland
Filipe Barata
ETH Zurich, Zurich, Switzerland
Florian von Wangenheim
ETH Zurich, Zurich, Switzerland
Elgar Fleisch
ETH Zurich, Zurich, Switzerland
Wolfgang Weinmann
University of Bern, Bern, Switzerland
Felix Wortmann
University of St. Gallen, St. Gallen, Switzerland
DOI

10.1145/3706598.3714007

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714007

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Autonomus Vehicle

Annex Hall F204
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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