Visible to the public How much do you Trust your Self-Driving Car? Exploring Human-Robot Trust in High-Risk Scenarios

TitleHow much do you Trust your Self-Driving Car? Exploring Human-Robot Trust in High-Risk Scenarios
Publication TypeConference Paper
Year of Publication2020
AuthorsXu, J., Howard, A.
Conference Name2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Date PublishedOct. 2020
ISBN Number978-1-7281-8526-2
Keywordsartificial intelligence, cybernetics, decision making, Human Behavior, human factors, human-robot interaction, human-robot trust, Market research, pubcrawl, resilience, Resiliency, Robot Trust, robots, self-driving car, Task Analysis

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants' decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants' trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Citation Keyxu_how_2020