  Autonomous vehicles are expected to navigate in complex traffic scenarios
with multiple surrounding vehicles. The correlations between road users vary
over time, the degree of which, in theory, could be infinitely large, thus
posing a great challenge in modeling and predicting the driving environment. In
this paper, we propose a method to model multi-vehicle interactions using a
stochastic vector field model and apply non-parametric Bayesian learning to
extract the underlying motion patterns from a large quantity of naturalistic
traffic data. We then use this model to reproduce the high-dimensional driving
scenarios in a finitely tractable form. We use a Gaussian process to model
multi-vehicle motion, and a Dirichlet process to assign each observation to a
specific scenario. We verify the effectiveness of the proposed method on
highway and intersection datasets from the NGSIM project, in which complex
multi-vehicle interactions are prevalent. The results show that the proposed
method can capture motion patterns from both settings, without imposing heroic
prior, and hence demonstrate the potential application for a wide array of
traffic situations. The proposed modeling method could enable simulation
platforms and other testing methods designed for autonomous vehicle evaluation,
to easily model and generate traffic scenarios emulating large scale driving
data.
