# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 3 Personalized Driving Behavior Analysis Model
### Semantic Division of the Driving Behavior  
In order to better divide the semantics of the driving behavior, this study classifies the three characteristic variables into different classes according to the human velocity difference perception threshold, vestibular threshold, and kinesthetic threshold. The relative distances are classified into three classes [26], \(\{LD,ND,CD\}\), and the relative speed and acceleration are each classified into five classes [27]: \(\{RFB,FB,KE,CI,RCI\}\) and \(\{AA,GA,NA,GD,AD\}\). In the next section, the driving behavior semantics are utilized to evaluate the similarity between drivers. The classification criteria can be seen in Table 1.  
According to the hidden state sequence of MHMM, this study obtains the trajectory segmentation results. Due to the space limitation, this study only shows the trajectory segmentation results for driver #1, as shown in Figure 5. The horizontal coordinates represent the time in seconds, and different color blocks represent different types of driving behavior semantics.  
Figure 5 shows an example of the segmentation results using MHMM. As shown in Figure 5, MHMM can divide the driving speed sequence into various driving behavior segments. Effective behavior semantics are extracted based on the data distribution features. Besides this, the behavioral semantic segments are not very short (durations less than 1.0 s) because MHMM can better reduce the influence of random noise in the data compared with the traditional HMM [11]. The results show that MHMM can not only divide car  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Variable State** & **Threshold** \\ \hline \multirow{3}{*}{Relative Distance (m)} & Long distance (LD) & \textgreater{}59.33 \\  & Normal distance (ND) & (27.04, 59.33) \\  & Close distance (CD) & (5, 27.04) \\ \hline \multirow{3}{*}{Relative Speed (m/s)} & Rapidly closing in (RCI) & \textless{}\textless{}\texttext{}\text{}\text{}\text{}\text{}\text{}\text{} \text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}  
following data into multiple driving behavior semantics, but can also keep them within a reasonable duration.  
Figure 5 indicates that when the distance from the lead vehicle is normal, the speed of the lead vehicle is less than that of the subject vehicle, and the subject vehicle rapidly closes in on the lead vehicle, then aggressively decelerates to fall behind from the lead vehicle, then slowly picks up speed to close in the lead vehicle and keeps a certain distance from it. At this time, the speed of the lead vehicle decreases, and the subject vehicle aggressively decelerates to fall behind from the lead vehicle. In this car-following segment, the driver kept a normal distance from the lead vehicle. The analysis process of the other two figures is similar to that of the first figure, which shows the driving behavior of the corresponding driver in this car-following segment.  
Table 2 shows the statistical results of the durations of the driving behavior semantic. We found that the HMM obtained a larger number of driving behavior semantic segments than the MHMM. In addition, the MHMM obtained a lower percentage of semantic segments with durations less than 1.0 s than the HMM, which indicates that the MHMM can keep the semantic segments within a reasonable duration range better, and can match the actual driving habits.