  Efficient behavior and trajectory planning is one of the major challenges for
automated driving. Especially intersection scenarios are very demanding due to
their complexity arising from the variety of maneuver possibilities and other
traffic participants. A key challenge is to generate behaviors which optimize
the comfort and progress of the ego vehicle but at the same time are not too
aggressive towards other traffic participants. In order to maintain real time
capability for courteous behavior and trajectory planning, an efficient
formulation of the optimal control problem and corresponding solving algorithms
are required. Consequently, a novel planning framework is presented which
considers comfort and progress as well as the courtesy of actions in a
graph-based behavior planning module. Utilizing the low level trajectory
generation, the behavior result can be further optimized for driving comfort
while satisfying constraints over the whole planning horizon. According
experiments show the practicability and real time capability of the framework.
