Multimodal sensor datasets play a crucial role in training machine learning algorithms for autonomous vehicles. While several such datasets exist, almost all of them deal with roads in urban settings. In this paper, we present Rural Road Detection Dataset (R2D2), a comprehensive collection of labeled point clouds for object detection and semantic segmentation of rural roads that aims to address this challenge. Our dataset includes a diverse range of rural environments and road types, providing a challenging environment for machine learning algorithms to learn from. With over 10,000 labeled point clouds captured from various locations, R2D2 is a valuable resource for researchers and practitioners working towards a safer and more efficient transportation systems in rural areas. We believe that our dataset will help to accelerate the development of autonomous driving in these remote areas and ultimately bring us closer to a future where all roads, no matter how rural, can be safely and efficiently navigated.


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    Titel :

    R2D2: Rural Road Detection Dataset


    Beteiligte:
    Ninan, Stephen (Autor:in) / Rathinam, Sivakumar (Autor:in) / Mittal, Harsh (Autor:in) / Sunny, Benjamin (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    3836821 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch