CRII: HCC: Understanding Trust Transfer and Dynamics Across Automation Levels

Automated vehicles (AVs) can improve road safety, enhance mobility, and reduce emissions. However, trust is key to their adoption. Overtrust can lead to complacency and accidents, fueling public skepticism and reducing confidence in automation. Undertrust results in disuse, preventing AVs from delivering their intended benefits. Miscalibrated trust remains a major barrier, particularly in partially automated systems where humans and automation share control in fluid and evolving ways. This raises a critical question: How does trust transfer between different degrees of automation? This project seeks to understand trust propagation across automation levels and driving contexts, ensuring trust is appropriately calibrated to support safe and effective human-automation interaction. This project has three research aims: (1) to develop a framework for trust transfer across automation levels and driving contexts, (2) to estimate trust transferability based on human-machine interface (HMI) design, and (3) to build a dynamic computational model for trust using automation level activation, environmental context, and interface design. Trust transfer will be examined through survey studies, driving simulations, and computational modeling. The survey study will assess how trust propagates across automation levels and the role of environmental factors in this process. A driving simulation study will track real-time trust shifts using behavioral, physiological, and eye-tracking data, focusing on how HMI similarity influences trust transfer. These findings will inform the development of a computational model that quantifies trust adaptation over time. The model will be trained and validated with empirical data to ensure predictive accuracy, providing a foundation for designing adaptive human-machine interfaces that enhance trust calibration and automation usability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.