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Objective This study examines how driving styles of fully automated vehicles affect drivers' trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers' interventions. Background Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle's driving style might have an important influence. Method A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation's driving style and the person's driving style affected the frequency and magnitude of their pedal depression. Results The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers' trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application We offer a measure and method for assessing driving styles.

Objective Understanding the factors that affect drivers' response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency. Background Drivers' responses to takeover requests can be assessed using the time it takes the driver to take over control. However, all the takeover timing studies that we could find focused on the mean response time. Method A study using an advanced driving simulator evaluated the effect of takeover request timing, event type at the onset of a takeover, and visual demand on drivers' response time. A mixed effects model was fit to the data using Bayesian quantile regression. Results Takeover request timing, event type that precipitated the takeover, and the visual demand all affect driver response time. These factors affected the 85th percentile differently than the median. This was most evident in the revealed stopped vehicle event and conditions with a longer time budget and scenes with lower visual demand. Conclusion Because the factors affect the quantiles of the distribution differently, a focus on the mean response can misrepresent actual system performance. The 85th percentile is an important performance metric because it reveals factors that contribute to delayed responses and potentially dangerous outcomes, and it also indicates how well the system accommodates differences between drivers.