Assessing Drivers' Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude.

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.

Moving Into the Loop: An Investigation of Drivers' Steering Behavior in Highly Automated Vehicles.

OBJECTIVE: This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering-drivers steering when the vehicle is under automated control. BACKGROUND: Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers' steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles. METHOD: A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners' manuals. RESULTS: The adaptive algorithm increased vicarious steering (p < .001), but instructed responsibility did not (p = .67), and there was no interaction between the algorithm and the responsibility (p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control. CONCLUSION: Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement.