  Planning for autonomous driving in complex, urban scenarios requires accurate
prediction of the trajectories of surrounding traffic participants. Their
future behavior depends on their route intentions, the road-geometry, traffic
rules and mutual interaction, resulting in interdependencies between their
trajectories. We present a probabilistic prediction framework based on a
dynamic Bayesian network, which represents the state of the complete scene
including all agents and respects the aforementioned dependencies. We propose
Markovian, context-dependent motion models to define the interaction-aware
behavior of drivers. At first, the state of the dynamic Bayesian network is
estimated over time by tracking the single agents via sequential Monte Carlo
inference. Secondly, we perform a probabilistic forward simulation of the
network's estimated belief state to generate the different combinatorial scene
developments. This provides the corresponding trajectories for the set of
possible, future scenes. Our framework can handle various road layouts and
number of traffic participants. We evaluate the approach in online simulations
and real-world scenarios. It is shown that our interaction-aware prediction
outperforms interaction-unaware physics- and map-based approaches.
