Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000614982Publikationsstatus
publishedExterne Links
Buchtitel
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing SystemsSeiten / Artikelnummer
Verlag
Association for Computing MachineryKonferenz
Thema
head movements; eye movements; safety; alcohol; driving; health; driver monitoringFörderung
183569 - Design and Evaluation of a Vehicle Hypoaglycemia Warning System in Diabetes (HEADWIND Project) (SNF)
Zugehörige Publikationen und Daten
Is part of: https://doi.org/10.3929/ethz-b-000634620