Trust

Due to the increased integration of robots into industrial, service, and educational settings it is important to understand how and why individuals interact with robots. The current study aimed to explore the extent to which individuals are receptive to nonverbal communication from a robot compared to a human, and the individual differences and stimuli attributes that are related to trust ratings. A combination of eyetracking and survey measures were used to collect data, and a robot and human both performed the same gesture to allow for direct comparison of gaze patterns. Individuals utilized the offered information equivalently from agents. Survey measures indicated that trust ratings significantly differed between agents, and the perceived likability and intelligence of the agent were the greatest predictors of increased trust.

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.