  Autonomous agents must be able to safely interact with other vehicles to
integrate into urban environments. The safety of these agents is dependent on
their ability to predict collisions with other vehicles' future trajectories
for replanning and collision avoidance. The information needed to predict
collisions can be learned from previously observed vehicle trajectories in a
specific environment, generating a traffic model. The learned traffic model can
then be incorporated as prior knowledge into any trajectory estimation method
being used in this environment. This work presents a Gaussian process based
probabilistic traffic model that is used to quantify vehicle behaviors in an
intersection. The Gaussian process model provides estimates for the average
vehicle trajectory, while also capturing the variance between the different
paths a vehicle may take in the intersection. The method is demonstrated on a
set of time-series position trajectories. These trajectories are reconstructed
by removing object recognition errors and missed frames that may occur due to
data source processing. To create the intersection traffic model, the
reconstructed trajectories are clustered based on their source and destination
lanes. For each cluster, a Gaussian process model is created to capture the
average behavior and the variance of the cluster. To show the applicability of
the Gaussian model, the test trajectories are classified with only partial
observations. Performance is quantified by the number of observations required
to correctly classify the vehicle trajectory. Both the intersection traffic
modeling computations and the classification procedure are timed. These times
are presented as results and demonstrate that the model can be constructed in a
reasonable amount of time and the classification procedure can be used for
online applications.
