Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol

TL;DR

This paper explores passive vision-based methods using multi-modal sensors to detect alcohol impairment in drivers, aiming to improve road safety by early identification of drunk driving through advanced machine learning analysis.

Contribution

It introduces a multi-modal sensor system and machine learning models for passive detection of alcohol impairment in drivers using visual, thermal, audio, and chemical data.

Findings

Analyzed the impact of alcohol on driving performance in a simulator.

Developed models for detecting alcohol impairment from multi-modal sensor data.

Discussed machine learning phenomena relevant to future experiments.

Abstract

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.