Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps

TL;DR

This paper introduces a primitive-based framework using a Nonhomogeneous Hidden Markov Model and time-series clustering to analyze merging behavior patterns at freeway on-ramps, aiding autonomous vehicle decision-making.

Contribution

It presents a novel approach that considers exogenous variables in modeling merging behavior evolution, providing deeper insights into traffic flow dynamics.

Findings

Identified interpretable merging behavior patterns.

Revealed the influence of covariates on pattern evolution.

Enhanced understanding of merging processes in congested traffic.

Abstract

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.