The model proposed by [PERSON] explicitly considers the different scenarios when the preceding vehicle is a truck or a passenger car, while implicitly taking into account the human driver's preferences and habits. The main statements of these models (including their type, equation, heterogeneity factors embedded, strength, and weakness) are also summarized in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Car-Following** & **Conclusion** & **Author** \\
**Heterogeneity Factors** & & \\ \hline Type of following car & (1) The speed of truck drivers is more constant than that of passenger & \\ Type of following car & car drivers; (2) Truck drivers tend to maintain a larger following & \\  & distance from their leading car compared to passenger car drivers. & \\ Type of leading car & Following a larger vehicle results in a greater TTC (time to collision), & \\  & THW (time headway), and safety margin for the following vehicle. & \\ Traffic flow & Different traffic states can influence driving styles and THW. & [PERSON] et al. (2018) and [PERSON] et al. (2018) \\ Driving style & Drivers of passenger cars differ with respect to their driving styles. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Heterogeneity factors on car-following behavior. As shown in Table 2, these studies have taken into account heterogeneity factors in modeling the following behavior, but there are still research gaps in human-likeness and model methods. 1. Current car-following models are too limited in consideration of human-likeness. In theory-driven models, only a few factors are typically included due to the increased complexity that arises from incorporating additional parameters. Furthermore, quantifying certain factors into physically meaningful parameters can be challenging. In data-driven models, the current focus of research primarily revolves around using deep learning models to directly emulate human behavior, often overlooking the consideration of heterogeneity factors. As mentioned before, research on behavioral heterogeneity factors has been extensively analyzed to determine whether they have an impact on car-following behavior. However, these factors are not fully considered in the construction of car-following models in the field of autonomous driving. Thus, in order to achieve a more human-like effect, we add these factors to the car-following model so that the model can learn this knowledge through machine learning and make human-like responses. 2. Current car-following models mainly use theory-driven models and deep learning models; the choice of models can be expanded. Incorporating heterogeneity factors into theory-driven models often leads to more parameters, making model calibration challenging. Deep learning models have good performance, but they have complex structures, resource-intensive requirements, and low interpretability. Ensemble learning models provide a promising avenue for further exploration. It can imitate human behavior through machine learning, and it can also embed other heterogeneous factors artificially. Crucially, the ensemble learning model has strong learning ability and is lightweight, which are two of the key factors applied in actual autonomous driving systems. Therefore, in order to increase the practicality of data-driven car-following models in actual autonomous driving systems, we explore more ensemble learning decision-tree models for car-following modeling. In summary, to bridge the research gaps in these two aspects, the purpose of our research is to try to embed behavioral heterogeneity factors into the car-following model to achieve more human-like car-following performance. At the same time, we apply