Dynamic Trust and Reliance in Malfunctioning Driving Automation and Risky Environments

This work studies how drivers form, update, and act on trust while interacting with conditional driving automation. The core question is whether reliance matches system capability, especially when automation performance varies and drivers must decide when to intervene.

Through empirical research studies, this project is aimed at answering the following research questions:

• How do drivers adjust their reliance decisions when automated vehicle performance changes across situations?
• What behavioral and physiological indicators best predict calibrated trust, over-reliance, and under-reliance?
• How can computational models explain and predict trust adaptation in risky or malfunctioning automation environments?