Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers. Issue 41 (2022) - Ortiz, Kendric R.; Thorpe, Adam J.; Perez, AnaMaria; Luster, Maya; Pitts, Brandon J.; Oishi, Meeko
Abstract: 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 participants 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 participants. We conducted a pilot study with 6 participants in an advanced driving simulator, in which participants 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.