Automated vehicles need to detect and classify objects and traffic participants accurately. Reliable object classification using automotive radar sensors has proved to be challenging. We propose a method that combines classical radar signal processing and Deep Learning algorithms. The range-azimuth information on the radar reflection level is used to extract a sparse region of interest from the range-Doppler spectrum. This is used as input to a neural network (NN) that classifies different types of stationary and moving objects. We present a hybrid model (DeepHybrid) that receives both radar spectra and reflection attributes as inputs, e.g. radar cross-section. Experiments show that this improves the classification performance compared to models using only spectra. Moreover, a neural architecture search (NAS) algorithm is applied to find a resource-efficient and high-performing NN. NAS yields an almost one order of magnitude smaller NN than the manually-designed one while preserving the accuracy. The proposed method can be used for example to improve automatic emergency braking or collision avoidance systems.


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    Titel :

    DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification


    Beteiligte:
    Cozma, Adriana-Eliza (Autor:in) / Morgan, Lisa (Autor:in) / Stolz, Martin (Autor:in) / Stoeckel, David (Autor:in) / Rambach, Kilian (Autor:in)


    Erscheinungsdatum :

    2021-09-19


    Format / Umfang :

    1119005 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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