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Variability in human response creates non-trivial challenges for modeling andcontrol of human-automation systems. As autonomy becomes pervasive, methodsthat can accommodate human variability will become paramount, to ensureefficiency, safety, and high levels of performance. We propose an easilycomputable modeling framework which takes advantage of a metric to assessvariability in individual human response in a dynamic task that subjects repeatover several trials. Our approach is based in a transformation of observedtrajectories to a reproducing kernel Hilbert space, which captures variabilityin human response as a distribution embedded within the Hilbert space. Weevaluate the similarity across responses via the maximum mean discrepancy,which measures the distance between distributions within the Hilbert space. Weapply this metric to a difficult driving task designed to elucidate differencesacross subjects. We conducted a pilot study with 6 subjects in an advanceddriving simulator, in which subjects were tasked with collision avoidance of anobstacle in the middle of the road, around a blind corner, in a nighttimescenario, while steering only with the non-dominant hand.

Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers