Modeling mandatory and discretionary lane changes using dynamic interaction networks

TL;DR

This paper introduces a novel framework combining hidden Markov models and graph analysis to differentiate and understand the interaction patterns in mandatory and discretionary lane changes, aiding autonomous vehicle decision-making.

Contribution

It develops a new analytical framework that decomposes lane change interactions into segments and characterizes their network structures, revealing differences between MLC and DLC.

Findings

MLC are more complex with intense interactions.

DLC are more random with no clear evolution rules.

Multiple heterogeneous interaction network structures exist during lane changes.

Abstract

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.