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

Abstract

Understanding human merging behavior patterns at freeway on-ramps is important for assisting 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 in congested traffic flow at freeway on-ramps. 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 used to group 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 proposed approach can deeply analzye the complicated merging processes and provide interpretable merging behavior patterns as well as the evolutionary mechanism. The findings in this study can be useful for designing and improving the merging decision-making of autonomous vehicles.