The Design and Integration of a Comprehensive Measurement System to Assess Trust in Automated Driving

Abstract

With the increased availability of commercially automated vehicles, trust in automation may serve a critical role in the overall system safety, rate of adoption, and user satisfaction. We developed and integrated a novel measurement system to better calibrate human-vehicle trust in driving. The system was designed to collect a comprehensive set of measures based on a validated model of trust focusing on three types: dispositional, learned, and situational. Our system was integrated into a Tesla Model X to assess different automated functions and their effects on trust and performance in real-world driving (e.g., lane changes, parking, and turns). The measurement system collects behavioral, physiological (eye and head movements), and self-report measures of trust using validated instruments. A vehicle telemetry system (Ergoneers Vehicle Testing Kit) uses a suite of sensors for capturing real driving performance data. This off-the-shelf solution is coupled with a custom mobile application for recording driver behaviors, such as engaging/disengaging automation, during on-road driving. Our initial usability evaluations of components of the system revealed that the system is easy to use, and events can be logged quickly and accurately. Our system is thus viable for data collection and can be used to model user trust behaviors in realistic on-road conditions.