  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 0.05g/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.
