  In complex lane change (LC) scenarios, semantic interpretation and safety
analysis of dynamic interactive pattern are necessary for autonomous vehicles
to make appropriate decisions. This study proposes an unsupervised learning
framework that combines primitive-based interactive pattern recognition methods
and risk analysis methods. The Hidden Markov Model with the Gaussian mixture
model (GMM-HMM) approach is developed to decompose the LC scenarios into
primitives. Then the Dynamic Time Warping (DTW) distance based K-means
clustering is applied to gather the primitives to 13 types of interactive
patterns. Finally, this study considers two types of time-to-collision (TTC)
involved in the LC process as indicators to analyze the risk of the interactive
patterns and extract high-risk LC interactive patterns. The results obtained
from The Highway Drone Dataset (highD) demonstrate that the identified LC
interactive patterns contain interpretable semantic information. This study
explores the spatiotemporal evolution law and risk formation mechanism of the
LC interactive patterns and the findings are useful for comprehensively
understanding the latent interactive patterns, improving the rationality and
safety of autonomous vehicle's decision-making.
