Lane detection is the core problem of autonomous driving. After completing lane recognition, the autonomous driving system can realize the active safety and control function of vehicle lateral movement. However, the existing methods cannot adapt well to various environments and generate many unnecessary points, resulting in low detection accuracy. In this paper, Point Instance Network (PINet) based on key points estimation and instance segmentation is used, which is composed of several stacked hourglass networks that are trained at the same time. Compared with existing algorithms, PINet achieves ideal accuracy and false positive rate on CULane, especially in night and dazzle light.


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    Titel :

    Application of Lane Detection Based on Point Instance Network in Autonomous Driving


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sun, Fengchun (Herausgeber:in) / Yang, Qingxin (Herausgeber:in) / Dahlquist, Erik (Herausgeber:in) / Xiong, Rui (Herausgeber:in) / Liu, Jialin (Autor:in) / Yu, Quanqing (Autor:in) / Zhu, Pengyu (Autor:in)

    Kongress:

    International Conference on Energy Storage and Intelligent Vehicles ; 2022 December 03, 2022 - December 04, 2022



    Erscheinungsdatum :

    11.05.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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    Hu, Peirong / Xu, Kun / Qiao, Jia et al. | IEEE | 2023


    Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

    Ko, Yeongmin / Lee, Younkwan / Azam, Shoaib et al. | IEEE | 2022



    LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

    Wang, Ze / Ren, Weiqiang / Qiu, Qiang | ArXiv | 2018

    Freier Zugriff

    A Point-based MDP for Robust Single-Lane Autonomous Driving Behavior under Uncertainties

    Wei, Junqing / Dolan, John M. / Snider, Jarrod M. et al. | Tema Archiv | 2011