  Variability in human response creates non-trivial challenges for modeling and
control of human-automation systems. As autonomy becomes pervasive, methods
that can accommodate human variability will become paramount, to ensure
efficiency, safety, and high levels of performance. We propose an easily
computable modeling framework which takes advantage of a metric to assess
variability in individual human response in a dynamic task that subjects repeat
over several trials. Our approach is based in a transformation of observed
trajectories to a reproducing kernel Hilbert space, which captures variability
in human response as a distribution embedded within the Hilbert space. We
evaluate the similarity across responses via the maximum mean discrepancy,
which measures the distance between distributions within the Hilbert space. We
apply this metric to a difficult driving task designed to elucidate differences
across subjects. We conducted a pilot study with 6 subjects in an advanced
driving simulator, in which subjects were tasked with collision avoidance of an
obstacle in the middle of the road, around a blind corner, in a nighttime
scenario, while steering only with the non-dominant hand.
