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In complex lane change (LC) scenarios, semantic interpretation and safetyanalysis of dynamic interactive pattern are necessary for autonomous vehiclesto make appropriate decisions. This study proposes an unsupervised learningframework that combines primitive-based interactive pattern recognition methodsand risk analysis methods. The Hidden Markov Model with the Gaussian mixturemodel (GMM-HMM) approach is developed to decompose the LC scenarios intoprimitives. Then the Dynamic Time Warping (DTW) distance based K-meansclustering is applied to gather the primitives to 13 types of interactivepatterns. Finally, this study considers two types of time-to-collision (TTC)involved in the LC process as indicators to analyze the risk of the interactivepatterns and extract high-risk LC interactive patterns. The results obtainedfrom The Highway Drone Dataset (highD) demonstrate that the identified LCinteractive patterns contain interpretable semantic information. This studyexplores the spatiotemporal evolution law and risk formation mechanism of theLC interactive patterns and the findings are useful for comprehensivelyunderstanding the latent interactive patterns, improving the rationality andsafety of autonomous vehicle's decision-making.

V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes