  About 30% of all traffic crash fatalities in the United States involve drunk
drivers, making the prevention of drunk driving paramount to vehicle safety in
the US and other locations which have a high prevalence of driving while under
the influence of alcohol. Driving impairment can be monitored through active
use of sensors (when drivers are asked to engage in providing breath samples to
a vehicle instrument or when pulled over by a police officer), but a more
passive and robust mechanism of sensing may allow for wider adoption and
benefit of intelligent systems that reduce drunk driving accidents. This could
assist in identifying impaired drivers before they drive, or early in the
driving process (before a crash or detection by law enforcement). In this
research, we introduce a study which adopts a multi-modal ensemble of visual,
thermal, audio, and chemical sensors to (1) examine the impact of acute alcohol
administration on driving performance in a driving simulator, and (2) identify
data-driven methods for detecting driving under the influence of alcohol. We
describe computer vision and machine learning models for analyzing the driver's
face in thermal imagery, and introduce a pipeline for training models on data
collected from drivers with a range of breath-alcohol content levels, including
discussion of relevant machine learning phenomena which can help in future
experiment design for related studies.
