Detection of radar blockage is a critical safety function for automotive radar. In this paper, we report on a machine-learning approach to classify the blockage condition in automotive radar, using detection data. We consider logistic regression, tree-bagging, and neural network approaches. We used pruning to reduce the size of the neural network to make it a viable option for embedded processors with limited memory. The results show that the classifiers, especially the neural network, can achieve high accuracy with a low false-alarm rate.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine Learning Applied to Blockage Classification in Automotive Radar


    Beteiligte:
    Fetterman, Matt (Autor:in) / Carlsen, Aret (Autor:in) / Ru, Jifeng (Autor:in) / Zuo, Yifan (Autor:in)


    Erscheinungsdatum :

    2020-11-23


    Format / Umfang :

    3073154 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Blockage Effects in Automotive Wind-Tunnel Testing

    Carr, G. W. / Stapleford, W. R. | SAE Technical Papers | 1986


    Blockage effects in automotive wind-tunnel testing

    Carr,G.W. / Stapleford,W.R. / MIRA,Nuneaton,GB | Kraftfahrwesen | 1986


    RAILROAD CROSSING BLOCKAGE MACHINE

    INOUE TAKAFUMI / FUJITA HIROYOSHI | Europäisches Patentamt | 2017

    Freier Zugriff

    Downstream blockage corrections of automotive cooling fan module performance

    Hunt,A.G. / Savory,E. / Gifford,N.L. et al. | Kraftfahrwesen | 2009


    Downstream Blockage Corrections of Automotive Cooling Fan Module Performance

    Hunt, A. G. / Savory, E. / Gifford, N. L. et al. | SAE Technical Papers | 2009