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

TL;DR

This paper introduces a novel, computationally efficient framework for quantifying individual human response variability during obstacle avoidance maneuvers in driving, aiding the development of safer autonomous systems.

Contribution

It presents a new method using reproducing kernel Hilbert space and maximum mean discrepancy to measure and analyze within-driver variability in dynamic tasks.

Findings

Effective variability measurement in driving responses

Pilot study with 6 subjects demonstrating method applicability

Potential for improving human-automation system safety

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 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.