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Examining graphs for similarity is a well-known challenge, but one that ismandatory for grouping graphs together. We present a data-driven method tocluster traffic scenes that is self-supervised, i.e. without manual labelling.We leverage the semantic scene graph model to create a generic graph embeddingof the traffic scene, which is then mapped to a low-dimensional embedding spaceusing a Siamese network, in which clustering is performed. In the trainingprocess of our novel approach, we augment existing traffic scenes in theCartesian space to generate positive similarity samples. This allows us toovercome the challenge of reconstructing a graph and at the same time obtain arepresentation to describe the similarity of traffic scenes. We could show,that the resulting clusters possess common semantic characteristics. Theapproach was evaluated on the INTERACTION dataset.

Self Supervised Clustering of Traffic Scenes using Graph Representations