  In a given scenario, simultaneously and accurately predicting every possible
interaction of traffic participants is an important capability for autonomous
vehicles. The majority of current researches focused on the prediction of an
single entity without incorporating the environment information. Although some
approaches aimed to predict multiple vehicles, they either predicted each
vehicle independently with no considerations on possible interaction with
surrounding entities or generated discretized joint motions which cannot be
directly used in decision making and motion planning for autonomous vehicle. In
this paper, we present a probabilistic framework that is able to jointly
predict continuous motions for multiple interacting road participants under any
driving scenarios and is capable of forecasting the duration of each
interaction, which can enhance the prediction performance and efficiency. The
proposed traffic scene prediction framework contains two hierarchical modules:
the upper module and the lower module. The upper module forecasts the intention
of the predicted vehicle, while the lower module predicts motions for
interacting scene entities. An exemplar real-world scenario is used to
implement and examine the proposed framework.
