  There is an increase in interest to model driving maneuver patterns via the
automatic unsupervised clustering of naturalistic sequential kinematic driving
data. The patterns learned are often used in transportation research areas such
as eco-driving, road safety, and intelligent vehicles. One such model capable
of modeling these patterns is the Hierarchical Dirichlet Process Hidden
Semi-Markov Model (HDP-HSMM), as it is often used to estimate data
segmentation, state duration, and transition probabilities. While this model is
a powerful tool for automatically clustering observed sequential data, the
existing HDP-HSMM estimation suffers from an inherent tendency to overestimate
the number of states. This can result in poor estimation, which can potentially
impact impact transportation research through incorrect inference of driving
patterns. In this paper, a new robust HDP-HSMM (rHDP-HSMM) method is proposed
to reduce the number of redundant states and improve the consistency of the
model's estimation. Both a simulation study and a case study using naturalistic
driving data are presented to demonstrate the effectiveness of the proposed
rHDP-HSMM in identifying and inference of driving maneuver patterns.
