Detection of alcohol-involved driving via machine learning using eye-tracking and vehicle data

Introduction: Alcohol impaired driving remains a significant public safety problem, prompting the United States and the EU to move towards the inclusion of impaired driving prevention technology in future vehicle production. A large body of literature suggests acute alcohol use produces changes in ocular behavior and vehicle control, suggesting driver monitoring system (DMS) data can be used to detect alcohol-impaired driving. Method: We use DMS data from a simulated driving study to assess the role of different monitoring lengths, data inputs, and machine learning methods on the accurate identification of alcohol presence using a baseline-controlled study with no alcohol at baseline, followed by four drives after alcohol dosing with BrACs of 0.10, 0.085, 0.07, and 0.055 g/210 L. Results: Using data from an 8-minute segment of rural driving, we find convolution-based feature extraction methods using vehicle and ocular inputs can differentiate between samples of sober and alcohol-dosed driving with a high degree of accuracy (AUC = 0.955), outperforming models using only ocular inputs (AUC = 0.916) and only vehicle inputs (AUC = 0.880). When applied to new driving environments, models involving ocular features continue to perform well (AUC > 0.95), while models using only vehicle-based features are less predictive. We find pupil and blink behavior are the most influential predictors of alcohol presence. Future research should consider a broader range of BrACs and fatigue combinations, particularly if a DMS aims to differentiate between alcohol-impaired and fatigued states. Practical Application: As policymakers explore the feasibility of DMS to prevent alcohol-involved accidents, we find these systems should focus on longer measurement durations and utilize both vehicle and eye data (especially blink and pupil behavior) to achieve optimal performance. Furthermore, we find that machine learning models trained to detect patterns in eye data can be successfully applied in new driving environments without retraining.