\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Model Equation/Category** & \begin{tabular}{c} **Heterogeneity** \\ **Factors** \\ \end{tabular} & **Strength** & **Weakness** & **Author** \\ \hline Theory-driven & \(a_{n}(t)=\) & Traffic flow & Explicit model & Parameter calibration & [25] \\ model & \(a_{n}\frac{V_{n}(t-\varphi_{n})^{Rg}}{N_{n}(t-\varphi_{n})^{Rg}}R_{n}(t- \varphi_{n})^{Rg}V_{n}(t-\varphi_{n})^{Rg}+\varepsilon_{n}^{g}(t)\) & Driving habit & Low latency & & [PERSON] [26] \\ Theory-driven & \(a_{n}=a(\Delta X_{n}/\Delta\widetilde{X}_{n})+\beta(\Delta V_{n})+\lambda+\varepsilon\) & Driving habit & & & \\ \hline Data-driven & & & & & \\ model & multilayer GRUs & \begin{tabular}{c} Drivers’ \\ preferences \\ \end{tabular} & & & & & \\ (deep-learning) & & & & & \\ Data-driven & & & & & \\ model & GRU & \begin{tabular}{c} Drivers’ \\ preferences \\ \end{tabular} & & & & & \\ (deep-learning) & & & & & \\ Data-driven & & & & & \\ model & LSTM & \begin{tabular}{c} Drivers’ \\ preferences \\ \end{tabular} & & & & & \\ (deep-learning) & & & & & \\ \hline Data-driven & & & & & \\ model (ensemble learning) & & & & & \\ \hline \hline \end{tabular} \begin{tabular}{c} Data-driven \\ model (ensemble learning) & local linear model tree (LOLIMOT) model & \
\begin{tabular}{c} Type of \\ following \\ vehicle \\ \end{tabular} &
\begin{tabular}{c} Strong learning \\ ability to \\ imitate human \\ behavior; \\ Lightweight; \\ Interpretable \\ \end{tabular} & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the car-following models considered heterogeneity factors. the ensemble learning method to expand the breadth of practical application of the car-following model in autonomous driving. In this paper, we first incorporate heterogeneity factors into the car-following model. Secondly, we use an interpretable machine learning model to build a car-following model based on the HighD (Highway Driving Dataset for Autonomous Driving) dataset. Finally, we conduct a comparison between car-following models that consider heterogeneity and those that do not and quantify the impact of heterogeneity factors on car-following behavior. Our contributions are highlighted as follows:  
1. Incorporating the heterogeneity factors of car-following behavior into the car-following model to achieve more human-like car-following performance. 2. Apply decision tree-based ensemble learning algorithms for the data-driven car-following model, which can partially overcome the issues of deep learning models' lack of interpretability and high latency. 3. This paper quantifies the impact of heterogeneity factors on car-following behavior. That helps researchers better understand the effect of heterogeneity in car-following modeling. The remainder of this paper is organized as follows: In Section 2, we introduce the decision tree-based ensemble learning method that will be used in this paper. Then, three different encoding methods are introduced for the heterogeneity factor. Section 3 analyzes the heterogeneity of car-following behavior in the HighD dataset and applies the proposed model to predict the car-following behavior of drivers. We conclude this paper with a discussion in Section 4.