Can we adapt to highly automated vehicles as passengers? The mediating effect of trust and situational awareness on role adaption moderated by automated driving style

The emergence of highly automated driving technology provides safe and convenient travel while also causing user inadaptation. Therefore, based on human factors engineering, it is necessary to study highly automated vehicles (HAVs) that meet different user needs. Thus, this study aims to investigate the relationships between state anxiety, situational awareness, trust, and role adaptation. The adaptation model is constructed to conduct a study on the adaptation of HAVs with different automated styles when user roles change from driver to passenger. Simulated riding was conducted in the HAV experiment (N = 117), collecting scale data after each participant had experienced each automated driving style. A structural equation modeling approach was applied to analyze the adaptation model based on scale data. The results showed that there was a significant correlation between state anxiety, situational awareness, trust, and role adaptation. State anxiety has a significant negative predictive effect on trust, situational awareness, and role adaptation. In addition to its direct impact on role adaptation, state anxiety also has an indirect effect on role adaptation through situational awareness and trust. Furthermore, the automated driving style has been confirmed to have a moderating role in the relationship between the direct and indirect effects of state anxiety and role adaptation. Our findings contribute to multiple streams of the literature and have important implications for designing personalized automated driving to improve user acceptance.