Measuring and Understanding Trust in Motion: Behavioral Archetypes in Human-Automated Guided Vehicle Interactions

As robots are rapidly entering new aspects of human life, it is essential to understand how humans trust them while ensuring appropriate safety guarantees from the outset. However, during the early phases, identifying the full range of trust-driven behaviors humans may exhibit in interactions with robots remains challenging due to limited data. Therefore, capturing high-level similarities and differences among individuals can help robots acclimate to individual users over time. Supporting this process raises an important question: what type of data can better support such acclimation—subjective trust ratings, trust-driven physiological data, or a combination of both? To address this question, a human subject study is conducted in a virtual manufacturing environment where participants interact with Automated Guided Vehicles (AGVs) during task execution. From these interactions, patterns of trust-driven behaviors and subjective trust ratings are independently extracted. Behavioral patterns reveal two distinct clusters, termed Skeptical and Deliberate, while subjective ratings yield two groups corresponding to high and low trust. It was found that behavioral and subjective clustering produce fundamentally different group distinctions, indicating limited correspondence between the two. Moreover, differences in behavioral clusters correspond to significantly different crossing orders, i.e., whether the participant crosses before the AGV or yields to it. At the same time, such distinctions are only statistically significant when the user crosses first under subjective clustering. Finally, high-level design implications for transport robots have been outlined to help them gradually adapt their behavior to different groups of people as they gather more data to build better, personalized models.