  Trust is crucial for ensuring the safety, security, and widespread adoption
of automated vehicles (AVs), and if trust is lacking, drivers and the public
may not be willing to use them. This research seeks to investigate trust
profiles in order to create personalized experiences for drivers in AVs. This
technique helps in better understanding drivers' dynamic trust from a persona's
perspective. The study was conducted 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, and a miss
condition with eight takeover requests (TORs) in different scenarios. Drivers'
dispositional trust, initial learned trust, dynamic trust, personality, and
emotions were measured. We identified three trust profiles (i.e., believers,
oscillators, and disbelievers) using a K-means clustering model. In order to
validate this model, we built a multinomial logistic regression model based on
SHAP explainer that selected the most important features to predict the trust
profiles with an F1-score of 0.90 and accuracy of 0.89. We also discussed how
different individual factors influenced trust profiles which helped us
understand trust dynamics better from a persona's perspective. Our findings
have important implications for designing a personalized in-vehicle trust
monitoring and calibrating system to adjust drivers' trust levels in order to
improve safety and experience in automated driving.
