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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 …