Clustering Roadway Departures from Impaired Driving Studies using Derived Features and Shape-Based Methods

Abstract

Lane departures remain a major contributor to fatal traffic accidents despite the increasing adoption of lane departure warning systems. Existing research has focused on the detection and mitigation of lane departures, while the characteristics of departures and their potential relationships with categories of driver impairment have received less attention. We apply a variety of unsupervised clustering approaches to a comprehensive set of lane departures from three drug-impaired driving studies involving combinations of cannabis and low-dose alcohol, alcohol, and cannabis. We find that departures are more frequent in active-drug conditions, ranging from 26% to 222% increases in mean departures per minute of driving depending on the study and condition. Across all studies, departures are most effectively grouped into two clusters based on duration and peak lateral deviation, with average silhouette scores of the most effective approaches being 0.778, 0.759, and 0.871, respectively, indicating high degrees of cohesion within clusters and separation between them. After clustering on the principal components derived from driving data from before, during, and after each departure we find clusters that differ significantly in their relative proportions of alcohol-dosed drivers (p < 0.001). Shape-based approaches also identify two clusters of departures that differ in duration, maximum deviation, and timing of peak deviation. These clusters contain a significantly different composition of dosing conditions in the combined alcohol and cannabis study (p = 0.015). Our results suggest lane departures can be effectively profiled and that leveraging their characteristics may benefit driver monitoring systems targeting the detection of drug-impaired driving.