  Properly calibrated human trust is essential for successful interaction
between humans and automation. However, while human trust calibration can be
improved by increased automation transparency, too much transparency can
overwhelm human workload. To address this tradeoff, we present a probabilistic
framework using a partially observable Markov decision process (POMDP) for
modeling the coupled trust-workload dynamics of human behavior in an
action-automation context. We specifically consider hands-off Level 2 driving
automation in a city environment involving multiple intersections where the
human chooses whether or not to rely on the automation. We consider automation
reliability, automation transparency, and scene complexity, along with human
reliance and eye-gaze behavior, to model the dynamics of human trust and
workload. We demonstrate that our model framework can appropriately vary
automation transparency based on real-time human trust and workload belief
estimates to achieve trust calibration.
