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

Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Models