## 4 Dynamic Driving Style Recognition Method
### Driving Style Recognition Based on GMM-SVM  
The clustering of driving styles is a common application of cluster analysis, which is performed using both K-means clustering and hierarchical clustering methods. However, the K-means clustering method is only applicable to cases that can be divided linearly. In comparison to other clustering methods, the GMM can handle the shape and size of clusters in a more flexible manner, and assign different weights to each cluster to reflect their relative importance [45]. In addition, the GMM expresses the likelihood of a data point belonging to a specific cluster in probabilistic form, thereby allowing for more flexible assumptions within the model. Furthermore, after fitting the trajectory data using the Gaussian mixture clustering model, the acceleration distribution of drivers belonging to each category of driving style during driving can be output. This allows for the simulation of limited rational behavioral decisions made by human drivers, which is a key advantage of the Gaussian mixture clustering approach. Due to the spatial-temporal inconsistency of driving styles, this study clusters driving styles for each of the three zones. For clustering analysis, feature vectors were extracted from the game scenarios of selected road sections, including the minimum distance between vehicle pairs, the average value of speed, the variance in speed, the average value of acceleration, and the maximum value of jerk. The driving styles are clustered into three categories: the driving style that defines a small following distance and sharp speed fluctuation is aggressive; the one that defines the maximum following distance is conservative; and the rest are moderate. The second merging zone (MZ2) provides an illustrative example. Figure 5 depicts the distribution curves of the characteristic parameters of the three types of driving styles. The blue, orange, and green curves represent the distribution of the parameters of normal drivers, aggressive drivers, and conservative drivers, respectively. It is evident that there are notable discrepancies in the way drivers operate their vehicles, contingent upon their individual driving styles. Figure 4: Distribution of merging locations and diagram of merging zones. Figure 3: Video screenshot and data acquisition. The original data, augmented with driving style categories, may be employed as labeled data to train the driving style recognition model. In this section, the Support Vector Machine (SVM) model is selected as the driving style recognition model for training. The SVM is a binary classification model, and its fundamental principle is to identify a separating hyperplane that can accurately differentiate the training dataset and has the largest geometric interval [46]. There are an infinite number of such hyperplanes for datasets that can be linearly divided (i.e., perceptual machines), but the separating hyperplane with the largest geometric interval is unique. The key idea of SVM is to find an optimal hyperplane in high-dimensional space to separate different classes of data points. This is achieved by finding the closest data points, i.e., support vectors, and maximizing their distance (interval) to the hyperplane. Due to the constraints of the length of the article and the prevalence of GMM and SVM as clustering and classification algorithms, the principles, and designs of GMM and SVM will not be elucidated in exhaustive detail. The experimental results of Gaussian mixture clustering are used to calculate clustering centers for each class of driving style, including average speed center \(v_{center}\), variance in the speed center \(par_{center}\), average acceleration center \(a_{center}\), maximum value of jerk \(jerk\_max_{center}\), and minimum distance center \(d_{center}\). This is in order to prevent overfitting such that the trained model performs too well in the training dataset but performs poorly in unknown cases.