Research on the influencing factors of human Drivers' Trust in fully autonomous vehicles

초록

Fully Autonomous Vehicles (FAVs), as a key driver of future mobility, have demonstrated the potential to reduce traffic accidents. However, the factors influencing human drivers' trust in FAVs remain unclear. A critical issue lies in identifying these factors, particularly how the design features of FAVs affect trust. This study proposes a conceptual model incorporating empathy, information transparency, subjective knowledge, social influence, perceived risk, self-efficacy, and trust. A survey of 175 respondents was conducted, and the results were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). By examining how perceived risk and self-efficacy contribute to human drivers' trust in FAVs and the interrelations among these constructs, this study offers deeper insights into the psychological underpinnings of trust in fully autonomous vehicles. The findings reveal that trust is significantly influenced by empathy, information transparency, social influence, subjective knowledge, perceived risk, and self-efficacy. However, the effect of information transparency on perceived risk is not significant, nor is there a significant linear relationship between empathy and self-efficacy. Overall, this study enhances our understanding of human drivers' trust in FAVs and provides valuable guidance for policymakers and technology developers to devise targeted optimization strategies aimed at effectively calibrating such trust.

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