Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving


    Beteiligte:
    Bijelic, Mario (Autor:in) / Gruber, Tobias (Autor:in) / Ritter, Werner (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    2543861 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    BENCHMARKING IMAGE SENSORS UNDER ADVERSE WEATHER CONDITIONS FOR AUTONOMOUS DRIVING

    Bijelic, Mario / Gruber, Tobias / Ritter, Werner | British Library Conference Proceedings | 2018


    Object Detection of Autonomous Vehicles under Adverse Weather Conditions

    Arthi, V. / Murugeswari, R. / P, Nagaraj | IEEE | 2022


    Determination of Changes in Autonomous Vehicle Location Under Adverse Weather Conditions

    KAMINITZ YAAKOV | Europäisches Patentamt | 2023

    Freier Zugriff

    SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions

    El-Shair, Zaid A. / Abu-raddaha, Abdalmalek / Cofield, Aaron et al. | IEEE | 2024


    Automated driving recognition technologies for adverse weather conditions

    Keisuke Yoneda / Naoki Suganuma / Ryo Yanase et al. | DOAJ | 2019

    Freier Zugriff