| Summary: | Vehicle Platoons (VPs) organize multiple vehicles to travel together, forming an efficient and coordinated moving group. For the foreseeable future, VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flows, with frequent instances of VPs cutting into the trajectories of HDVs. To effectively understand and manage this cut-in behavior, it is crucial to grasp the underlying driving styles. Based on real-world natural vehicle trajectory data, this study explores the classification and analysis of driving styles during the cut-in process in vehicles, especially on highway segments. The research initially preprocesses a real-world natural dataset, including wavelet denoising. Fourteen features are selected from the speed-change preparation phase and lane-change phase, covering the state of the vehicle itself and its interaction with other vehicles. Next, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used to reduce the dimensionality, followed by clustering driving styles using the K-means++ algorithm. The results show that for the selected features, t-SNE technique shows better clustering effect than PCA as a whole, with optimal clustering performance at four clusters. Therefore, we categorize the driving styles of highway segment vehicle cut-in into four types. Finally, the characteristics of cut-in process of different driving styles are analyzed statistically, which shows that it is effective to classify driving styles based on real natural data set.
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