  Analysis of heterogeneous patterns in complex spatio-temporal data finds
usage across various domains in applied science and engineering, including
training autonomous vehicles to navigate in complex traffic scenarios.
Motivated by applications arising in the transportation domain, in this paper
we develop a model for learning heterogeneous and dynamic patterns of velocity
field data. We draw from basic nonparameric Bayesian modeling elements such as
hierarchical Dirichlet process and infinite hidden Markov model, while the
smoothness of each homogeneous velocity field element is captured with a
Gaussian process prior. Of particular focus is a scalable approximate inference
method for the proposed model; this is achieved by employing sequential MAP
estimates from the infinite HMM model and an efficient sequential GP posterior
computation technique, which is shown to work effectively on simulated data
sets. Finally, we demonstrate the effectiveness of our techniques to the NGSIM
dataset of complex multi-vehicle interactions.
