Designing Trustworthy Autonomous Systems: Integrating Multi-Dimensional Trust Measures for Advanced Air Mobility Systems

Developing trust in human-robot interaction is vital for improving user performance and system reliability. This study builds on trust quantification research to create a trustworthy simulation environment using the “Auto Safe” tool, which integrates environmental and user-based trust measures. The tool employs the DBSCAN algorithm to evaluate obstacle density and classify safety zones while adapting its behavior based on user profiles. The study examines trust evolution during gameplay and explores ways to modify the simulator for earlier trust calibration, enhancing safety and performance. Initial results, compared to baseline simulations, demonstrate improved user trust, reduced collision rates, and increased reliance on automation. While the sample size is limited, these findings provide promising directions for scalable trust calibration in advanced air mobility systems.