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Volume 35 Issue 3
May  2020
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Dun Liang, Yuan-Chen Guo, Shao-Kui Zhang, Tai-Jiang Mu, Xiaolei Huang. Lane Detection: A Survey with New Results[J]. Journal of Computer Science and Technology, 2020, 35(3): 493-505. DOI: 10.1007/s11390-020-0476-4
Citation: Dun Liang, Yuan-Chen Guo, Shao-Kui Zhang, Tai-Jiang Mu, Xiaolei Huang. Lane Detection: A Survey with New Results[J]. Journal of Computer Science and Technology, 2020, 35(3): 493-505. DOI: 10.1007/s11390-020-0476-4
shu

Lane Detection: A Survey with New Results

  • 1 Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing 100084, China;
    2 College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, U.S.A.

Funds: This work was supported by the National Natural Science Foundation of China under Grant Nos. 61902210 and 61521002, a research grant from the Beijing Higher Institution Engineering Research Center, and the Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.
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  • Author Bio:

    Dun Liang is a Ph.D. candidate in the Department of Computer Science and Technology at Tsinghua University, Beijing, where he received his B.S. degree in computer science and technology, in 2016. His research interests include computer graphics, visual media and high-performance computing.

  • Corresponding author:

    Tai-Jiang Mu E-mail:taijiang@tsinghua.edu.cn

  • Received Date: March 27, 2020
  • Revised Date: April 16, 2020
  • Published Date: May 27, 2020
    Abstract
  • Lane detection is essential for many aspects of autonomous driving, such as lane-based navigation and highdefinition (HD) map modeling. Although lane detection is challenging especially with complex road conditions, considerable progress has been witnessed in this area in the past several years. In this survey, we review recent visual-based lane detection datasets and methods. For datasets, we categorize them by annotations, provide detailed descriptions for each category, and show comparisons among them. For methods, we focus on methods based on deep learning and organize them in terms of their detection targets. Moreover, we introduce a new dataset with more detailed annotations for HD map modeling, a new direction for lane detection that is applicable to autonomous driving in complex road conditions, a deep neural network LineNet for lane detection, and show its application to HD map modeling.
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    • References (49)
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