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Traffic scenario categorisation is an essential component of automateddriving, for e.\,g., in motion planning algorithms and their validation.Finding new relevant scenarios without handcrafted steps reduce the requiredresources for the development of autonomous driving dramatically. In this work,a method is proposed to address this challenge by introducing a clusteringtechnique based on a novel data-adaptive similarity measure, called RandomForest Activation Pattern (RFAP) similarity. The RFAP similarity is generatedusing a tree encoding scheme in a Random Forest algorithm. The clusteringmethod proposed in this work takes into account that there are labelledscenarios available and the information from the labelled scenarios can help toguide the clustering of unlabelled scenarios. It consists of three steps.First, a self-supervised Convolutional Neural Network~(CNN) is trained on allavailable traffic scenarios using a defined self-supervised objective. Second,the CNN is fine-tuned for classification of the labelled scenarios. Third,using the labelled and unlabelled scenarios an iterative optimisation procedureis performed for clustering. In the third step at each epoch of the iterativeoptimisation, the CNN is used as a feature generator for an unsupervised RandomForest. The trained forest, in turn, provides the RFAP similarity to adaptiteratively the feature generation process implemented by the CNN. Extensiveexperiments and ablation studies have been done on the highD dataset. Theproposed method shows superior performance compared to baseline clusteringtechniques.

Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity