Understanding Human Drivers' Trust in Highly Automated Vehicles via Structural Equation Modeling

Abstract

Highly automated vehicles are expected to become commonplace shortly. Driving authority is switched between the automated driving system and the human driver for highly automated vehicles. The appropriate level of drivers' trust in highly automated vehicles (THAV) plays an essential role in the safety of the switching process. Hence, the assessment of THAV and the investigation of its influencing factors are necessary for highly automated vehicles. In this paper, a second-order measurement model for THAV was established based on exploratory factor analysis and confirmatory factor analysis. Then, the affecting factors of THAV were systematically explored based on structural equation modeling. The results indicated that the proposed measurement model could effectively measure THAV. In addition, education, age, and driving experience had significant effects on THAV, while gender and accident experience showed insignificant effects on THAV. This study contributes to a systematic understanding of drivers' trust in highly automated vehicles, the development of human-centered automated driving systems, and enhancing the acceptance of highly automated vehicles.