  Motion prediction for automated vehicles in complex environments is a
difficult task that is to be mastered when automated vehicles are to be used in
arbitrary situations. Many factors influence the future motion of traffic
participants starting with traffic rules and reaching from the interaction
between each other to personal habits of human drivers. Therefore we present a
novel approach for a graph-based prediction based on a heterogeneous holistic
graph representation that combines temporal information, properties and
relations between traffic participants as well as relations with static
elements like the road network. The information are encoded through different
types of nodes and edges that both are enriched with arbitrary features. We
evaluated the approach on the INTERACTION and the Argoverse dataset and
conducted an informative ablation study to demonstrate the benefit of different
types of information for the motion prediction quality.
