The development of Autonomous Vehicles (AVs) today requires precise and reliable detection of road line markings. Indeed, recognizing road line markings from camera images acquired by the vehicle plays a crucial role in ensuring its safe navigation and improving its driving performance. Road line detection is of key importance in real-time scenarios for navigation purposes, as well as offline for the generation of HD maps. In recent years, deep neural networks have proven effective in performing this task. In particular, Convolutional Neural Networks (CNNs) have helped develop multiple Advanced Driver Assistance Systems (ADAS), now fully integrated into common commercial vehicles. This paper presents a novel CNN-based pipeline for recognizing road line markings from front-view camera images in an online setup, and it shows how these detections can be aggregated offline into aerial-like maps as a first step toward the creation of HD maps. The proposed architecture comprises a multi-decoder to accurately classify image pixels representing different classes of road line markings, as well as those related to the drivable area. The mapping system then projects the extracted road line points into the Bird's-Eye View (BEV) space and integrates the extracted information with accurate localization measurements for georeferencing. Experimental evaluations on real-world data, including data acquired with instrumented vehicles, reveal the effectiveness of the proposed pipeline in both frame-by-frame detection and integrated mapping quality.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Semantic Bird's-Eye View Road Line Mapping




    Publication date :

    2023-09-24


    Size :

    4758593 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RoadBEV: Road Surface Reconstruction in Bird’s Eye View

    Zhao, Tong / Yang, Lei / Xie, Yichen et al. | IEEE | 2024


    BIRD'S EYE VIEW

    Online Contents | 1999


    BIRD'S EYE VIEW

    Online Contents | 1998


    BIRD'S EYE VIEW

    Online Contents | 1998


    BIRD'S EYE VIEW

    Online Contents | 1998