We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss function is designed to incorporate uncertainty estimations predicted by auxiliary output layers. Using our proposed method, the network ignores to train from noisy samples, and focuses more on informative ones. We validate our method on the KITTI object detection benchmark. Our method surpasses the baseline method which does not explicitly estimate uncertainties by up to nearly 9% in terms of Average Precision (AP). It also produces state-of-the-art results compared to other methods, while running with an inference time of only 72ms. In addition, we conduct extensive experiments to understand how aleatoric uncertainties behave. Extracting aleatoric uncertainties brings almost no additional computation cost during the deployment, making our method highly desirable for autonomous driving applications.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection


    Beteiligte:
    Feng, Di (Autor:in) / Rosenbaum, Lars (Autor:in) / Timm, Fabian (Autor:in) / Dietmayer, Klaus (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    1934416 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    LEVERAGING UNCERTAINTIES FOR DEEP MULTI-MODAL OBJECT DETECTION IN AUTONOMOUS DRIVING

    Feng, Di / Cao, Yifan / Rosenbaum, Lars et al. | British Library Conference Proceedings | 2020


    Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

    Feng, Di / Cao, Yifan / Rosenbaum, Lars et al. | IEEE | 2020


    Leveraging Anchor-Based LiDAR 3D Object Detection via Point Assisted Sample Selection

    Chen, Shitao / Zhang, Haolin / Zheng, Nanning | IEEE | 2025


    Real-time Pseudo-LiDAR 3D object detection with geometric constraints

    Li, Changcai / Meng, Haitao / Chen, Gang et al. | IEEE | 2022


    Real-time Spatial-temporal Context Approach for 3D Object Detection using LiDAR

    Kumar, K. S. Chidanand / Al-Stouhi, Samir | TIBKAT | 2020