  A quantitative understanding of dynamic lane-changing (LC) interaction
patterns is indispensable for improving the decision-making of autonomous
vehicles, especially in mixed traffic with human-driven vehicles. This paper
develops a novel framework combining the hidden Markov model and graph
structure to identify the difference in dynamic interaction networks between
mandatory lane changes (MLC) and discretionary lane changes (DLC). A hidden
Markov model is developed to decompose LC interactions into homogenous segments
and reveal the temporal properties of these segments. Then, conditional mutual
information is used to quantify the interaction intensity, and the graph
structure is used to characterize the connectivity between vehicles. Finally,
the critical vehicle in each dynamic interaction network is identified. Based
on the LC events extracted from the INTERACTION dataset, the proposed
analytical framework is applied to modeling MLC and DLC under congested traffic
with levels of service E and F. The results show that there are multiple
heterogeneous dynamic interaction network structures in an LC process. A
comparison of MLC and DLC demonstrates that MLC are more complex, while DLC are
more random. The complexity of MLC is attributed to the intense interaction and
frequent transition of the interaction network structure, while the random DLC
demonstrate no obvious evolution rules and dominant vehicles in interaction
networks. The findings in this study are useful for understanding the
connectivity structure between vehicles in LC interactions, and for designing
appropriate and well-directed driving decision-making models for autonomous
vehicles and advanced driver-assistance systems.
