  Dilemma zones at signalized intersections present a commonly occurring but
unsolved challenge for both drivers and traffic operators. Onsets of the yellow
lights prompt varied responses from different drivers: some may brake abruptly,
compromising the ride comfort, while others may accelerate, increasing the risk
of red-light violations and potential safety hazards. Such diversity in
drivers' stop-or-go decisions may result from not only surrounding traffic
conditions, but also personalized driving behaviors. To this end, identifying
personalized driving behaviors and integrating them into advanced driver
assistance systems (ADAS) to mitigate the dilemma zone problem presents an
intriguing scientific question. In this study, we employ a game engine-based
(i.e., CARLA-enabled) driving simulator to collect high-resolution vehicle
trajectories, incoming traffic signal phase and timing information, and
stop-or-go decisions from four subject drivers in various scenarios. This
approach allows us to analyze personalized driving behaviors in dilemma zones
and develop a Personalized Transformer Encoder to predict individual drivers'
stop-or-go decisions. The results show that the Personalized Transformer
Encoder improves the accuracy of predicting driver decision-making in the
dilemma zone by 3.7% to 12.6% compared to the Generic Transformer Encoder, and
by 16.8% to 21.6% over the binary logistic regression model.
