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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    Title :

    Semantic Segmentation on Automotive Radar Maps


    Contributors:


    Publication date :

    2019-06-01


    Size :

    2227068 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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