Autonomous vehicles should be capable of operating in all types of weather conditions. Drivable road region detection is a core component of the perception stack of self-driving vehicles. Current approaches for detecting road regions perform well in good weather but lack in inclement weather conditions. In this paper, we examine the effect of inclement weather on the camera-based state-of-the-art deep learning approaches and introduce a new camera and automotive radar-based multimodal deep learning model to efficiently detect drivable road regions in all weather conditions. We also propose a novel approach to overcome the sparse resolution problem of automotive radars and a way to effectively use it in higher precision tasks such as image segmentation. To validate our work, we have augmented the nuScenes data with rain and fog to add challenging weather conditions. Experimental results show that the performance of the state-of-the-art techniques drops 18% in bad weather conditions while our proposed method improves the performance by 12% compared to the state-of-the-art.


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    Titel :

    Deep Learning Based Road Boundary Detection Using Camera and Automotive Radar


    Beteiligte:


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    1019501 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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