  Current approaches to identifying driving heterogeneity face challenges in
comprehending fundamental patterns from the perspective of underlying driving
behavior mechanisms. The concept of Action phases was proposed in our previous
work, capturing the diversity of driving characteristics with physical
meanings. This study presents a novel framework to further interpret driving
patterns by classifying Action phases in an unsupervised manner. In this
framework, a Resampling and Downsampling Method (RDM) is first applied to
standardize the length of Action phases. Then the clustering calibration
procedure including ''Feature Selection'', ''Clustering Analysis'',
''Difference/Similarity Evaluation'', and ''Action phases Re-extraction'' is
iteratively applied until all differences among clusters and similarities
within clusters reach the pre-determined criteria. Application of the framework
using real-world datasets revealed six driving patterns in the I80 dataset,
labeled as ''Catch up'', ''Keep away'', and ''Maintain distance'', with both
''Stable'' and ''Unstable'' states. Notably, Unstable patterns are more
numerous than Stable ones. ''Maintain distance'' is the most common among
Stable patterns. These observations align with the dynamic nature of driving.
Two patterns ''Stable keep away'' and ''Unstable catch up'' are missing in the
US101 dataset, which is in line with our expectations as this dataset was
previously shown to have less heterogeneity. This demonstrates the potential of
driving patterns in describing driving heterogeneity. The proposed framework
promises advantages in addressing label scarcity in supervised learning and
enhancing tasks such as driving behavior modeling and driving trajectory
prediction.
