  In the realm of driving technologies, fully autonomous vehicles have not been
widely adopted yet, making advanced driver assistance systems (ADAS) crucial
for enhancing driving experiences. Adaptive Cruise Control (ACC) emerges as a
pivotal component of ADAS. However, current ACC systems often employ fixed
settings, failing to intuitively capture drivers' social preferences and
leading to potential function disengagement. To overcome these limitations, we
propose the Editable Behavior Generation (EBG) model, a data-driven
car-following model that allows for adjusting driving discourtesy levels. The
framework integrates diverse courtesy calculation methods into long short-term
memory (LSTM) and Transformer architectures, offering a comprehensive approach
to capture nuanced driving dynamics. By integrating various discourtesy values
during the training process, our model generates realistic agent trajectories
with different levels of courtesy in car-following behavior. Experimental
results on the HighD and Waymo datasets showcase a reduction in Mean Squared
Error (MSE) of spacing and MSE of speed compared to baselines, establishing
style controllability. To the best of our knowledge, this work represents the
first data-driven car-following model capable of dynamically adjusting
discourtesy levels. Our model provides valuable insights for the development of
ACC systems that take into account drivers' social preferences.
