  Accurate modeling of car-following behaviors is essential for various
applications in traffic management and autonomous driving systems. However,
current approaches often suffer from limitations like high sensitivity to data
quality and lack of interpretability. In this study, we propose GenFollower, a
novel zero-shot prompting approach that leverages large language models (LLMs)
to address these challenges. We reframe car-following behavior as a language
modeling problem and integrate heterogeneous inputs into structured prompts for
LLMs. This approach achieves improved prediction performance and
interpretability compared to traditional baseline models. Experiments on the
Waymo Open datasets demonstrate GenFollower's superior performance and ability
to provide interpretable insights into factors influencing car-following
behavior. This work contributes to advancing the understanding and prediction
of car-following behaviors, paving the way for enhanced traffic management and
autonomous driving systems.
