Lane Detection: A Survey with New Results

Dun Liang, Yuan Chen Guo, Shao Kui Zhang, Tai Jiang Mu, Xiaolei Huang

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Lane detection is essential for many aspects of autonomous driving, such as lane-based navigation and high-definition (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.

Original languageEnglish (US)
Pages (from-to)493-505
Number of pages13
JournalJournal of Computer Science and Technology
Issue number3
StatePublished - May 1 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics


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