In recent years, with the widespread use of machine learning theory and the availability of large-scale trajectory data, data-driven car-following models have made significant progress. Data-driven car-following models utilize machine learning tools to mine phenomena and underlying patterns within large amounts of car-following behavior data and then predict car-following behaviors. The feasibility of data-driven car-following models was initially verified in 1998 when [PERSON] [10] presented a car-following model based on a feedforward neural network. Subsequently, various machine learning car-following models have been proposed, such as those based on ensemble learning methods [11] and artificial neural networks [12; 13]. In the past several years, with the penetration of deep learning models into various fields [14], some data-driven car-following models based on deep learning theory have emerged. For instance, [PERSON] [15] proposed a data-driven car-following model based on the gated recurrent unit (GRU) network. This model takes the velocity, velocity differences, and position differences that were observed over a period of time as inputs and predicts the driver's car-following behavior at the next moment. The result showed that the model achieved high accuracy on the Next-Generation Simulation Program (NGSIM) dataset. [PERSON] [16] applied the long short-term memory (LSTM) model to car-following behavior modeling. By extracting statistical variables from the trajectory data within a special time window (2 s in this literature), the results showed that the model achieved better performance than the traditional Gipps model. The core idea in these papers is that by considering long-term sequence data in deep learning models, factors such as drivers' experiences and preferences can be automatically embedded into the model to achieve high-level prediction accuracy. Undoubtedly, the strong imitation ability of deep learning car-following models for human car-following behavior is one of their advantages. However, there are also certain limitations to consider. (1) High latency. Choosing to input long-term sequence trajectory data means that a more complex model structure is required to process it. [PERSON] [15] used 3 hidden layers with a total of 50 neurons (30-10-10 neuron structure) to process them, and [PERSON] [16] used seven hidden layers, which takes a longer processing time. (2) These models lack interpretability because they have no explicit model expression. These limitations may be unacceptable for vehicle driving systems that require real-time processing and explainable decision-making. Furthermore, the memory effect is only one aspect that affects car-following behavior. Importantly, the heterogeneity of car-following behavior should also be taken into account in car-following modeling [17]. Heterogeneity stems from differences among the agent's traffic flow, which are the heterogeneity of drivers and vehicle characteristics [17]. Heterogeneity refers to differences in behavior and characteristics between drivers and vehicles. [PERSON] [17; 18; 19] used trajectory data to confirm that heterogeneity in car-following behavior does exist. The heterogeneity of car-following behavior can be caused by car-following combinations, driving styles, and traffic flow. Four types of car-following combinations are divided into (1) Car-Car; (2) Car-Truck; (3) Truck-Truck; and (4) Truck-Car. The car-following behavior of truck drivers will be more robust than that of car drivers. It has been observed [19] that the speed of truck drivers is more consistent compared to that of passenger car drivers. This can be attributed to the larger weight of trucks, which makes them less agile. Additionally, it is plausible that truck drivers adopt a more assertive driving style, as they may be able to anticipate future traffic conditions better due to their heightened visibility and greater driving skills.