This work presents a behavior planning algorithm for automated driving in urban environments
with an uncertain and dynamic nature. The algorithm allows to consider the prediction
uncertainty (e.g. different intentions) perception uncertainty (e.g. occlusions) as well as
the uncertain interactive behavior of the other agents explicitly. Simulating the most likely
future scenarios allows to find an optimal policy online that enables non-conservative planning
under uncertainty.
