A lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open‐source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR‐camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios.


    Access

    Download


    Export, share and cite



    Title :

    Towards high‐definition vector map construction based on multi‐sensor integration for intelligent vehicles: Systems and error quantification


    Contributors:
    Hu, Runzhi (author) / Bai, Shiyu (author) / Wen, Weisong (author) / Xia, Xin (author) / Hsu, Li‐Ta (author)

    Published in:

    Publication date :

    2024-08-01


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Vector data encoding of high definition map data for autonomous vehicles

    WHEELER MARK DAMON | European Patent Office | 2019

    Free access

    Lane network construction using high definition maps for autonomous vehicles

    YANG LIN / CUI MING | European Patent Office | 2020

    Free access

    Lane Network Construction Using High Definition Maps for Autonomous Vehicles

    YANG LIN / CUI MING | European Patent Office | 2018

    Free access

    High definition map updates based on sensor data collected by autonomous vehicles

    WHEELER MARK DAMON / WU XIAQING JAMES / LUO WEI | European Patent Office | 2020

    Free access