Classification of Officers’ Driving Situations Based on Eye-Tracking and Driver Performance Measures

Abstract

Motor vehicle crashes are a leading cause of police officers' deaths in the line of duty. These crashes are mainly attributed to officers' use of in-vehicle technologies while driving, distraction, fatigue, and high-speed driving conditions. The objective of this study is to classify officers' driving situations using a combination of driver behavior and eye-tracking measures. The study compared three algorithms, including random forest (RF), support vector machine (SVM), and random Fourier features (RFF) to classify officers' driving situations (i.e., normal vs. pursuit driving) and in-vehicle technology use. The results suggested that driver behavior measures, combined with RF or SVM methods, are most promising for classifying officers' driving condition (accuracy of about 90%). However, it might be more efficient to apply RFF with driver behavior measures to classify officers' use of in-vehicle technologies while driving due to the time cost reduction of RFF as compared to SVM and RF algorithms. The findings can be applied to improve future police vehicles, training protocols, and to provide adaptive technology solutions to reduce officers' driving distraction and workload.