Categorisation of traffic scenarios is an important component of scenario-based development and validation of automated vehicles. This problem requires an open-world learning approach but most of the machine learning methods used for traffic scenario categorisation work under the closed-world assumption. A closed-world model will classify all the inputs to one of the classes from the training data. An open-world learning method can identify, collect and cluster unknown traffic scenarios and incrementally add new scenario categories to the already existing ones. In this work, a hierarchical architecture for open-world learning method is proposed. The open-world architecture consists of the following components: an open-set recognition model, storage buffer, outlier detection, class-conditioned generative replay model, and clustering method. The components in the architecture contain novel machine learning approaches to address the challenging open-world learning tasks, e.g., Extreme Value Theory (EVT) for open-set recognition, Random Forest Activation Patterns (RFAPs) for clustering, class-conditioned generative models for replay, and self-supervised pre-training for feature generation. The proposed architecture is tested using real-world and simulation-based datasets. The results show the performance advantages of the proposed method. Also, extensive analysis of each component of the hierarchical open-world architecture underlines their importance in the overall architecture.


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    Titel :

    Open-World Learning for Traffic Scenarios Categorisation


    Beteiligte:
    Balasubramanian, Lakshman (Autor:in) / Wurst, Jonas (Autor:in) / Botsch, Michael (Autor:in) / Deng, Ke (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.05.2023


    Format / Umfang :

    1753123 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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