Many studies have been conducted on single lane detection, but multi-lane detection is rarely addressed. The latter is more advantageous for applications such as autonomous navigation, unmanned vehicles, departure warning, and cruise control. In this paper, we propose a novel and robust multiple lane detection algorithm based on the road structure information, which contains five complementary constraints: length constraint, parallel constraint, distribution constraint, pair constraint and uniform width constraint. All the five constraints are incorporated into a Hough transform (HT) based unified framework to select lane candidates. Nearly 99% of the false alarm candidates in HT space can be removed. Moreover, a dynamic programming strategy is proposed to find the most rational solutions among the remaining candidates. This strategy can effectively deal with combination complexity and interferences introduced by multi-lane detection. Experimental results on the benchmark dataset and other collected data demonstrate that the proposed method can outperform the state-of-the-art approaches in both accuracy and efficiency.


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    Title :

    Multiple Lane Detection via Combining Complementary Structural Constraints


    Contributors:
    Luo, Sheng (author) / Zhang, Xiaoqin (author) / Hu, Jie (author) / Xu, Jinghua (author)


    Publication date :

    2021-12-01


    Size :

    3723487 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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