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Improved Lane Line Detection Algorithms Based on Incomplete Line Fitting

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Lane detection is a crucial component of autonomous driving, as it enables vehicles to accurately perceive their environment and safely navigate roads. However, existing lane detection algorithms often struggle to balance real-time processing and robustness. In this paper, we propose an improved sliding window lane detection algorithm that overcomes this challenge. Our algorithm begins by transforming the image into a bird’s-eye view using inverse perspective transformation and then generates a binary map using the Sobel operator. The algorithm then dynamically selects between quadratic curves and straight lines to fit the vacant portions of the left and right lane lines within the current window and determines the position of the next sliding window. Finally, a quadratic curve or straight line is dynamically selected to fit the entire lane trajectory using the least square method. Our testing results show that our algorithm can detect lane lines quickly while maintaining high robustness and accuracy. This algorithm has the potential to be a valuable contribution to the field of computer vision and image processing for autonomous driving.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Jilin University, Changchun, 130000, China

    QingYu Ren, BingRui Zhao, TingShuo Jiang & WeiZe Gao

Authors
  1. QingYu Ren
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  2. BingRui Zhao
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  3. TingShuo Jiang
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  4. WeiZe Gao
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Corresponding author

Correspondence to QingYu Ren .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ren, Q., Zhao, B., Jiang, T., Gao, W. (2023). Improved Lane Line Detection Algorithms Based on Incomplete Line Fitting. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_58

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  • DOI: https://doi.org/10.1007/978-981-99-4742-3_58

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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