Key points are not available for this paper at this time.
The successful integration of Autonomous Vehicles (AVs) into daily life is influenced by various individual and contextual factors. This study examines key factors affecting acceptance and trust in AVs by assessing how interactive experiences influence user perceptions and adoption. Utilizing a Virtual Reality (VR) environment to simulate real-world driving conditions, the research investigates the impact of different levels of vehicle interaction—manual control, semi-autonomous, fully autonomous, and taxi-riding modes—on user and passenger trust, anxiety, perceived ease of use, and overall acceptance. A dual-method approach was employed, combining objective data collection through eye-tracking, which measures engagement and vigilance, with subjective user feedback gathered via the Autonomous Vehicle Acceptance Model (AVAM) questionnaire. The findings reveal a complex relationship between user control and trust in AV systems. While semi-autonomous conditions that incorporate user participation in decision-making enhance trust and intention to use, fully autonomous conditions influence trust but not intention to use significantly. However, semi- autonomous conditions also elicited higher anxiety levels compared to fully autonomous modes, suggesting a trade-off between interaction and comfort. Engagement levels, as indicated by visual metrics such as gaze patterns, were found to be critical for understanding user acceptance. These results highlight the importance of considering both subjective perceptions and objective behaviors in developing AV interfaces. The study contributes significant insights into the cognitive underpinnings of AV acceptance and underscores the need for strategies that both enhance user trust and minimize anxiety by finding an optimal balance between automation and user control. Further research into this balance is recommended to better accommodate the evolving nature of user trust and the cognitive complexities associated with semi-autonomous systems.
Derakhshan et al. (Mon,) studied this question.