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Driver classification provides an efficient approach to isolating unique traits associated with specific driver types under various driving conditions. Several past studies use classification to identify behavior and driving styles; however, very few studies employ both measurable physiological changes and environmental factors. This study looked to address the shortcomings in driver classification research using a data-driven approach to assess driving tasks performed under varying mental workloads. Psychophysiological and driving performance changes experienced by drivers when engaged in simulated tasks of varying difficulty were coupled with machine-learning techniques to provide a more accurate estimate of the ground truth for behavioral classification. A driving simulator study consisting of six tasks was carefully designed to incrementally vary complexity between individual tasks. Ninety drivers were recruited to participate in both the subjective and driving components of this research. Positive and Negative Affect Schedule (PANAS), Cognitive Reflection Task (CRT), Interpersonal Reactivity Index (IRI), Empathy Assessment Index (EAI), Psychological Entitlement Scale (PES), 18-point Need for Cognition (NFC), and a basic demographic survey were administered. Time-series clustering using the Dynamic Time Warping (DTW) score within hierarchical clustering was applied to determine difference in driving styles. The use of pre-driving psychometric questionnaires to determine the most suitable metrics for predicting driving style outside of the automobile suggest promising results. The results of the binary logistic regression indicate that an individual's annual driving mileage, CRT score, age, IRI fantasy score, and EAI affective response score, contribute significantly towards predicting their driving style.

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