New Structured Data Collection Approach for Real-Time Trust Measurement In Human-Autonomous Vehicle Interactions

Most of recent studies indicate that people are negatively predisposed toward utilizing
autonomous systems. These findings highlight the necessity of conducting research
to better understand the evolution of trust between humans and growing autonomous technologies
such as self-driving cars (SDC). This research therefore presents a new approach
for real-time trust measurement between passengers and SDCs. We utilized a new structured
data collection approach along with a virtual reality (VR) SDC simulator to understand
how various autonomous driving scenarios can increase or decrease human trust and
how trust can be re-built in the case of incidental failures. To verify our methodology, we
designed and conducted an empirical experiment on 50 human subjects. The results of this
experiment indicated that most subjects could rebuild trust during a reasonable timeframe
after the system demonstrated faulty behavior. Furthermore, we discovered that the cultural
background and past trust-related experiences of the subjects affect how they lose or regain
their trust in SDCs. Our analysis showed that this model is highly effective for collecting
real-time data from human subjects and lays the foundation for more-involved future
research in the domain of human trust and autonomous driving.