  Trajectory prediction in traffic scenes involves accurately forecasting the
behaviour of surrounding vehicles. To achieve this objective it is crucial to
consider contextual information, including the driving path of vehicles, road
topology, lane dividers, and traffic rules. Although studies demonstrated the
potential of leveraging heterogeneous context for improving trajectory
prediction, state-of-the-art deep learning approaches still rely on a limited
subset of this information. This is mainly due to the limited availability of
comprehensive representations. This paper presents an approach that utilizes
knowledge graphs to model the diverse entities and their semantic connections
within traffic scenes. Further, we present nuScenes Knowledge Graph (nSKG), a
knowledge graph for the nuScenes dataset, that models explicitly all scene
participants and road elements, as well as their semantic and spatial
relationships. To facilitate the usage of the nSKG via graph neural networks
for trajectory prediction, we provide the data in a format, ready-to-use by the
PyG library. All artefacts can be found here:
https://github.com/boschresearch/nuScenes_Knowledge_Graph
