## 2 Materials and Methodology
### Data and Variables
#### 2.1.2 Data Pre-Processing  
The aim of this paper is to enhance the human-likeness of the car-following model by incorporating the behavioral heterogeneity factors mentioned earlier. To reduce the influence of other interference factors, such as the external environment and differences in locations, it is better to choose one recording to analyze. In this paper, the 46-th track  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Unit** \\ \hline ID & The ID of the track. The IDs are assigned in ascending order. & [-] \\  & The width of the post-processed bounding box of the vehicle. This & \\ Width & corresponds to & [m] \\  & the length of the vehicle. & \\  & The height of the post-processed bounding box of the vehicle. This & \\ Height & corresponds to & [m] \\  & the width of the vehicle. & \\ minXVelocity & Minimal velocity in the driving direction. & [m/s] \\ minDHW & The minimal distance headway (minDHW). This value is set to \(-1\) if & [m/s] \\  & no preceding vehicle exists. & \\ Class & The vehicle class of the tracked vehicle (car or truck). & [-] \\ \hline Frame & \multicolumn{2}{c}{The current frame.} & [-] \\ ID & The ID of the track. The IDs are assigned in ascending order. & [-] \\ precedingID & The ID of the preceding vehicle in the same lane. This value is set to 0 & [-] \\  & if no preceding vehicle exists. & \\ xVelocity & The longitudinal velocity is in the image coordinate system. & [m/s] \\ THW & The time headway. This value is set to 0 if no preceding vehicle exists. & [m] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key features of the HighD dataset.  
Figure 2: The recording setup of the HighD dataset.  
recording of location 1 has been selected as it exhibits the highest proportion of trucks and the total number of vehicles within the recordings.  
Subsequently, in order to extract car-following state data for our experiments, we performed data preprocessing. This involved a two-step approach:  
Firstly, we identified vehicles that were actively engaged in car-following. This was achieved by systematically examining the vehicle IDs in the HighD dataset files to identify cases where a vehicle had a preceding vehicle and the car-following duration was at least 15 frames. This filtering criterion was employed to exclude instances where the vehicle was potentially involved in lane-changing maneuvers or exiting the recording area.  
Secondly, trajectory data were further refined to ensure that drivers were in a steady car-following state. To achieve this, we applied the following filtering constraints:  
* Exclude distance headways larger than 150 m to guarantee the influence of the heading vehicle.
* Exclude the situation of the dangerous car following the scenario where the relative distance is less than 50 m and the relative speed is greater than 3 m/s.  
By employing these stringent filtering criteria, we obtained a refined subset of data that accurately captured steady car-following states. This subset serves as the foundation for our experiments and enables us to conduct a comprehensive analysis of car-following behaviors.  
In total, the dataset includes 1103 trajectories (207,417 samples extracted), as shown in Table 4.  
In our experiment, 80% of the data were used for training; the rest were used for tests.