The perception system is a critical element of autonomous driving, where real-time and accurate segmentation of drivable areas and lanes is essential for intelligent decision-making during vehicle operation. Current approaches primarily focus on minimizing background interference in images, often overlooking the importance of edge information. In response, this paper introduces the edge-guided multi-loss network (EdgeUNet) designed for drivable area and lane segmentation. EdgeUNet employs an encoder for feature extraction and a decoder specifically tailored for the segmentation tasks. The decoder incorporates a novel feature fusion module (FFM), multi-scale feature aggregation module (MFSA), edge extraction module (EEM), and edge-aware optimization module (EAO), facilitating efficient extraction and supervision through edge information. Our model demonstrates superior performance on the Berkeley deep drive (BDD100K) dataset, achieving state-of-the-art results with 99.3% mean pixel accuracy (mPA) and 54.8% mean intersection-over-union (MIoU) in the lane detection task. Additionally, ablation studies conducted on the TuSimple and KITTI datasets further validate the effectiveness and generalizability of EdgeUNet.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    EdgeUNet: Edge-Guided Multi-Loss Network for Drivable Area and Lane Segmentation in Autonomous Vehicles


    Beteiligte:
    Sheng, Xing (Autor:in) / Zhang, Jun-Zhe (Autor:in) / Wang, Zhen (Autor:in) / Duan, Zong-Tao (Autor:in)


    Erscheinungsdatum :

    01.02.2025


    Format / Umfang :

    3731141 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Autonomous Vehicles: A Survey on Sensor Fusion, Lane Detection and Drivable Area Segmentation

    Morkar, Tejas / Sonawane, Suyash / Mahajan, Aditya et al. | Springer Verlag | 2022


    AfforDrive: Detection of Drivable Area for Autonomous Vehicles

    Jain, Mahek / Kamat, Guruprasad / Bachari, Rochan et al. | TIBKAT | 2023


    DRIVABLE AREA SEGMENTATION IN DETERIORATING ROAD REGIONS FOR AUTONOMOUS VEHICLES USING 3D LIDAR SENSOR

    Ali, Abdelrahman / Gergis, Mark / Abdennadher, Slim et al. | British Library Conference Proceedings | 2021


    Drivable Area Segmentation in Deteriorating Road Regions for Autonomous Vehicles using 3D LiDAR Sensor

    Ali, Abdelrahman / Gergis, Mark / Abdennadher, Slim et al. | IEEE | 2021