Lane-Changing Style Classification of Human Drivers Based on Driving Behavioral Primitives

The realization of personalized lane-changing (LC) for intelligent vehicles (IVs) is important for enhancing the social acknowledgment, user acceptance, adaptability, and trust of IVs. The LC style classification of human drivers represents a crucial foundation for achieving personalized LC. Therefore, this study constructs an LC style classification method based on driving behavioral primitives, which enables the classified LC styles to fully embody the implicit behavioral semantics and patterns of human drivers. First, a disentangled sticky hierarchical Dirichlet process hidden Markov model is proposed for the LC behavioral segment segmentation. The model can suppress frequent transitions of the hidden states, and vector autoregression is used to accurately describe the LC explicit behavioral parameters. Subsequently, the K-shape is employed to cluster all LC behavior segments to obtain interpretable and reasonable LC behavior primitives. Then, clustering features based on the LC behavioral primitives are constructed. Finally, LC styles are classified using density peak clustering, which does not require a manual specification of the number of clustering centers. Verification is performed on the Next Generation Simulation dataset, and the results indicate that this method can accurately and reasonably classify LC styles. The quantitative comparison with four state-of-the-art methods further demonstrates the advantages of the proposed method in LC style classification and confirms the effectiveness of introducing LC behavioral primitives.