As radar sensors can measure an object's range and velocity with a high degree of precision, moving objects can be successfully classified, as well. Classifying stationary objects still needs a lot of research, however. In this paper, we use popular semantic segmentation networks in order to classify the vehicle's immediate infrastructure. To this end, a full 3D measurement is performed with a test vehicle equipped with four high resolution corner radar sensors. A preprocessed point cloud is transformed into various radar maps for input to a neural network. Simulations as well as real-world measurements show an overall intersection over union of 84 and 77%, respectively, as well as an overall accuracy of 95 and 90%, respectively, being a new benchmark for this young research field.


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    Titel :

    Semantic Segmentation on Automotive Radar Maps


    Beteiligte:
    Prophet, Robert (Autor:in) / Li, Gang (Autor:in) / Sturm, Christian (Autor:in) / Vossiek, Martin (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    2227068 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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