  While motion planning techniques for automated vehicles in a reactive and
anticipatory manner are already widely presented, approaches to cooperative
motion planning are still remaining. In this paper, we present an approach to
enhance common motion planning algorithms, that allows for cooperation with
human-driven vehicles. Unlike previous approaches, we integrate the prediction
of other traffic participants into the motion planning, such that the influence
of the ego vehicle's behavior on the other traffic participants can be taken
into account. For this purpose, a new cost functional is presented, containing
the cost for all relevant traffic participants in the scene. Finally, we
propose a path-velocity-decomposing sampling-based implementation of our
approach for selected scenarios, which is evaluated in a simulation.
