  Analysis and recognition of driving styles are profoundly important to
intelligent transportation and vehicle calibration. This paper presents a novel
driving style analysis framework using the primitive driving patterns learned
from naturalistic driving data. In order to achieve this, first, a Bayesian
nonparametric learning method based on a hidden semi-Markov model (HSMM) is
introduced to extract primitive driving patterns from time series driving data
without prior knowledge of the number of these patterns. In the Bayesian
nonparametric approach, we utilize a hierarchical Dirichlet process (HDP)
instead of learning the unknown number of smooth dynamical modes of HSMM, thus
generating the primitive driving patterns. Each primitive pattern is clustered
and then labeled using behavioral semantics according to drivers' physical and
psychological perception thresholds. For each driver, 75 primitive driving
patterns in car-following scenarios are learned and semantically labeled. In
order to show the HDP-HSMM's utility to learn primitive driving patterns, other
two Bayesian nonparametric approaches, HDP-HMM and sticky HDP-HMM, are
compared. The naturalistic driving data of 18 drivers were collected from the
University of Michigan Safety Pilot Model Deployment (SPDM) database. The
individual driving styles are discussed according to distribution
characteristics of the learned primitive driving patterns and also the
difference in driving styles among drivers are evaluated using the
Kullback-Leibler divergence. The experiment results demonstrate that the
proposed primitive pattern-based method can allow one to semantically
understand driver behaviors and driving styles.
