  Understanding the merging behavior patterns at freeway on-ramps is important
for assistanting the decisions of autonomous driving. This study develops a
primitive-based framework to identify the driving patterns during merging
processes and reveal the evolutionary mechanism at freeway on-ramps in
congested traffic flow. The Nonhomogeneous Hidden Markov Model is introduced to
decompose the merging processes into primitives containing semantic
information. Then, the time-series K-means clustering is utilized to gather
these primitives with variable-length time series into interpretable merging
behavior patterns. Different from traditional state segmentation methods (e.g.
Hidden Markov Model), the model proposed in this study considers the dependence
of transition probability on exogenous variables, thereby revealing the
influence of covariates on the evolution of driving patterns. This approach is
evaluated in the merging area at a freeway on-ramp using the INTERACTION
dataset. Results demonstrate that the approach provides an insight about the
complicated merging processes. The findings about interpretable merging
behavior patterns as well as the evolutionary mechanism can be used to design
and improve the merging decision-making for autonomous vehicles.
