Lane detection plays a vital part in autonomous driving. Conventional studies rely on less robust hand-craft features, while deep learning has improved the performance of lane detection to a great extent. Different from dominant methods based on semantic segmentation, this paper proposes an end-to-end framework named DevNet, which combines deviation awareness with semantic features based on point estimation. It consists of two modules to capture more representative features by integrating information of distance deviation and angle which helps to tackle diverse driving conditions in real environments, such as dim or shiny light conditions, crowdedness, and vanishing lanes. Experiments on public datasets indicate that the proposed method achieves favorable performance when compared with the state-of-the-art methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DevNet: Deviation Aware Network for Lane Detection


    Contributors:
    Yao, Ziying (author) / Wu, Xinkai (author) / Wang, Pengcheng (author) / Ding, Chuan (author)

    Published in:

    Publication date :

    2022-10-01


    Size :

    4939728 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    LANE DEVIATION SUPPRESSION APPARATUS AND LANE DEVIATION SUPPRESSION METHOD

    SUZUKI TERUHIKO | European Patent Office | 2018

    Free access

    LANE DEVIATION SUPPRESSION DEVICE AND LANE DEVIATION SUPPRESSION METHOD

    SUZUKI TERUHIKO | European Patent Office | 2018

    Free access

    Lane deviation detection method and device

    JIANG BO / HUANG ZHONGWEI / LIU SIYU et al. | European Patent Office | 2015

    Free access

    LANE DEVIATION MODE DETERMINATION DEVICE AND LANE DEVIATION ALARM DEVICE

    IMANISHI AKIRA | European Patent Office | 2018

    Free access

    TRAFFIC LANE DEVIATION WARNING DEVICE AND TRAFFIC LANE DEVIATION WARNING METHOD

    KASAME TOMOHIDE | European Patent Office | 2017

    Free access