  Trust calibration presents a main challenge during the interaction between
drivers and automated vehicles (AVs). In order to calibrate trust, it is
important to measure drivers' trust in real time. One possible method is
through modeling its dynamic changes using machine learning models and
physiological measures. In this paper, we proposed a technique based on machine
learning models to predict drivers' dynamic trust in conditional AVs using
physiological measurements in real time. We conducted the study in a driving
simulator where participants were requested to take over control from automated
driving in three conditions that included a control condition, a false alarm
condition, a miss condition with eight takeover requests (TORs) in different
scenarios. Drivers' physiological measures were recorded during the experiment,
including galvanic skin response (GSR), heart rate (HR) indices, and
eye-tracking metrics. Using five machine learning models, we found that eXtreme
Gradient Boosting (XGBoost) performed the best and was able to predict drivers'
trust in real time with an f1-score of 89.1%. Our findings provide good
implications on how to design an in-vehicle trust monitoring system to
calibrate drivers' trust to facilitate interaction between the driver and the
AV in real time.
