As technological advancements and lowered costs make self-driving cars available to more people, it becomes important to understand the dynamics of human-automation interactions for safety and efficacy. We used a dynamical approach to examine data from a previous study on simulated driving with an automated driving assistant. To maximize effect size in this preliminary study, we focused the current analysis on the two lowest and two highest-performing participants. Our visual comparisons were the utilization of the automated system and the impact of perturbations. Low-performing participants toggled and maintained reliance either on automation or themselves for longer periods of time. Decision making of high-performing participants was using the automation briefly and consistently throughout the driving task. Participants who displayed an early understanding of automation capabilities opted for tactical use. Further exploration of individual differences and automation usage styles will help to understand the optimal human-automation-team dynamic and increase safety and efficacy.

Quantitative analysis of the relationship between humans and automation becomes increasingly important as the reliance of humans upon automation becomes more commonplace. We present a literature review of key factors—categorized as effects from automation, operator, and the environment—that influence trust in or reliance upon automation. Those factors are treated as parameters in a dynamical systems analysis (DSA) model whose manipulation induces phase transitions in the decision to use automation. A review of the most recent dynamical models indicates a trend toward including increasingly more parameters in increasingly sophisticated models. We review the challenges inherent in that approach and suggest an alternative with precedence in the broader DSA literature that is more parsimonious and amenable to real-time analysis.

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