Characterizing the Effect of Mind Wandering on Braking Dynamics in Partially Autonomous Vehicles

Publication Date

7-29-2024

Document Type

Article

Publication Title

ACM Transactions on Cyber-Physical Systems

Volume

8

Issue

3

DOI

10.1145/3653678

Abstract

Partially autonomous driving systems may require the human driver to take control at any moment, yet by their design, they often cause difficulty with attention management. In this preliminary study, we propose a data- and dynamics-driven approach to characterize driving performance in a partially autonomous vehicle during a manual braking event, under attentive or mind wandering states. A 10-participant experiment was completed in an advanced driving simulator. We employ a non-parametric learning technique, conditional distribution embeddings, to the driving simulator data, to evaluate likelihood of successfully completing the braking maneuver, under both attentive and mind wandering states. Our approach shows a statistically significant difference in braking profiles during mind wandering and non-mind wandering episodes for each participant. Our results reveal that heterogeneity in driving performance may have important implications for the design of autonomy that is responsive to attentional states. Data-driven tools, such as the one proposed here, may be useful in designing participant-specific alerts and warnings for control handovers and other safety-critical maneuvers, because of their potential to accommodate heterogeneous response.

Funding Number

CNS-1836900

Funding Sponsor

National Science Foundation

Keywords

automated driving, Human cyber-physical systems, stochastic processes, stochastic reachability

Department

Industrial and Systems Engineering

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