This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-classification. Traditional radar-based DOGM often faces challenges in accurately distinguishing between static and dynamic objects. Our approach addresses this limitation by introducing a neural network-based DOGM state correction mechanism, designed as a semantic segmentation task, to refine the accuracy of the occupancy grid. Additionally a heuristic fusion approach is proposed which allows to enhance performance without compromising on safety. We extensively evaluate this hybrid architecture on the NuScenes Dataset, focusing on its ability to improve dynamic object detection as well grid quality. The results show clear improvements in the detection capabilities of dynamic objects, highlighting the effectiveness of the deep learning-enhanced state correction in radar-based DOGM.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping


    Beteiligte:
    Ronecker, Max Peter (Autor:in) / Diaz, Xavier (Autor:in) / Karner, Michael (Autor:in) / Watzenig, Daniel (Autor:in)


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    2166898 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Radar-based Dynamic Occupancy Grid Mapping and Object Detection

    Diehl, Christopher / Feicho, Eduard / Schwambach, Alexander et al. | IEEE | 2020


    Deep RADAR Inverse Sensor Models for Dynamic Occupancy Grid Maps

    Wei, Zihang / Yan, Rujiao / Schreier, Matthias | IEEE | 2023


    Occupancy Radar Grid

    Jakob, Lombacher / Laudt, Kilian / Hahn, Markus et al. | British Library Conference Proceedings | 2017


    Extending occupancy grid mapping for dynamic environments

    Wessner, Joseph / Utschick, Wolfgang | IEEE | 2018


    Environment Recognition with FMCW-LiDAR-based Dynamic Occupancy Grid Mapping

    Okuya, Tsubasa / Yoneda, Masaki / Ogawa, Takashi | IEEE | 2023