The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completeness and fidelity. This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Existing algorithms utilize handcrafted features whose quality relies on the judgments of human experts. Additionally, the related feature compression methods are not scalable for a large dataset. Our approach thoroughly considers the traffic elements, including both in-traffic agent objects and map information. Meanwhile, we proposed a self-supervised deep learning approach for spatial and temporal feature extraction to avoid biased data representation. With the newly designed driving data clustering evaluation metrics based on data-augmentation, the accuracy assessment does not require a human-labeled dataset, which is subject to human bias. Via such unprejudiced evaluation metrics, we have shown our approach surpasses the existing methods that rely on handcrafted feature extractions.


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    Titel :

    Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction


    Beteiligte:
    Zhao, Jinxin (Autor:in) / Fang, Jin (Autor:in) / Ye, Zhixian (Autor:in) / Zhang, Liangjun (Autor:in)


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    2163296 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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