The development of autonomous driving relies significantly on high-quality datasets. Currently, there is a pressing need for abundant, diverse, and realistic datasets to drive advancements in autonomous driving technology. In this paper, we present a multi-sensor dataset specifically designed for autonomous driving. It incorporates advanced sensors such as a 128-beam Lidar, solid state Lidar, and event cameras. It not only provides precise trajectory ground truth for localization tasks but also offers real-time driver behavior for research such as reinforcement learning. To ensure the dataset reflects the real world, we took into account variations in lighting conditions, weather. This deliberate inclusion aims to provide realistic scenarios for researchers to comprehensively evaluate and optimize the performance of autonomous driving algorithms. Through this dataset, researchers can delve deeper into the challenges faced by autonomous driving technology, thereby driving innovation and progress in the field.


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    Titel :

    MSDAD:A Multi-Sensor Dataset for Autonomous Driving


    Beteiligte:
    Zhang, Zhihe (Autor:in) / Nie, Hongtao (Autor:in) / Zhang, Yichi (Autor:in) / Wei, Hao (Autor:in) / Zhao, Rui (Autor:in) / Zhi, Peng (Autor:in) / Zhou, Qingguo (Autor:in) / Li, Yan (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    8005943 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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