Leveraging Driver Vehicle and Environment Interaction: Machine Learning Using Driver Monitoring Cameras to Detect Drunk Driving

Journal
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Type
conference paper
Date Issued
2023
Author(s)
;
Martin Maritsch
;
Eva Van Weenen
;
Stefan Feuerriegel
;
Matthias Pfäffli
;
;
Wolfgang Weinmann
;
DOI
Abstract
Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person’s blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n = 30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm.
Language
English
HSG Classification
contribution to scientific community
Pages
32
Official URL
URL