We introduce a method to classify ghost moving detections in automotive radar sensors for advanced driver assistance systems. A fully connected network is used to distinguish between real and false moving detections in the occupancy gridmaps. By using this architecture, we combine the local Doppler information, along with the spatial context of the surrounding scenario to classify the moving detections. A proof of concept experiment shows promising results with data from a test drive in an urban scenario.


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    Title :

    Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning




    Publication date :

    2019-04-01


    Size :

    1444370 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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