  Modern vehicles are equipped with increasingly complex sensors. These sensors
generate large volumes of data that provide opportunities for modeling and
analysis. Here, we are interested in exploiting this data to learn aspects of
behaviors and the road network associated with individual drivers. Our dataset
is collected on a standard vehicle used to commute to work and for personal
trips. A Hidden Markov Model (HMM) trained on the GPS position and orientation
data is utilized to compress the large amount of position information into a
small amount of road segment states. Each state has a set of observations, i.e.
car signals, associated with it that are quantized and modeled as draws from a
Hierarchical Dirichlet Process (HDP). The inference for the topic distributions
is carried out using HDP split-merge sampling algorithm. The topic
distributions over joint quantized car signals characterize the driving
situation in the respective road state. In a novel manner, we demonstrate how
the sparsity of the personal road network of a driver in conjunction with a
hierarchical topic model allows data driven predictions about destinations as
well as likely road conditions.
