Radar is an inevitable part of the perception sensor set for autonomous driving functions. It plays a gap-filling role to complement the shortcomings of other sensors in diverse scenarios and weather conditions.In this paper, we propose a Deep Neural Network (DNN) based end-to-end object detection and heading estimation framework using raw radar data. To this end, we approach the problem in both a "Data-centric" and "model-centric" manner. We refine the publicly available CARRADA [1] dataset and introduce Bivariate norm annotations. Besides, the baseline model is improved by a transformer [2] inspired cross-attention fusion and further center-offset maps are added to reduce localisation error. Our proposed model improves the detection mean Average Precision (mAP) by 5%, while reducing the model complexity by almost 23%. For comprehensive scene understanding purposes, we extend our model for heading estimation. The improved ground truth and proposed model is available at Github.


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    Titel :

    Object Detection and Heading Estimation from Radar Raw data


    Beteiligte:
    Kothari, Ravi (Autor:in) / Kariminezhad, Ali (Autor:in) / Mayr, Christian (Autor:in) / Zhang, Haoming (Autor:in)


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    3973923 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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