Machine Vision and Applications

, Volume 30, Issue 1, pp 111–124 | Cite as

Robust multi-lane detection and tracking using adaptive threshold and lane classification

  • Yeongho Son
  • Elijah S. Lee
  • Dongsuk KumEmail author
Original Paper


Many global automotive companies have been putting efforts to reduce traffic accidents by developing advanced driver assistance system (ADAS) as well as autonomous vehicles. Lane detection is essential for both autonomous driving and ADAS because the vehicle must follow the lane. However, existing lane detection algorithms have been struggling in achieving robust performance under real-world road conditions where poor road markings, surrounding obstacles, and guardrails are present. Therefore, in this paper, we propose a multi-lane detection algorithm that is robust to the challenging road conditions. To solve the above problems, we introduce three key technologies. First, an adaptive threshold is applied to extract strong lane features from images with obstacles and barely visible lanes. Next, since erroneous lane features can be extracted, an improved RANdom SAmple Consensus algorithm is introduced by using the feedback from lane edge angles and the curvature of lane history to prevent false lane detection. Finally, the lane detection performance is greatly improved by selecting only the lanes that are verified through the lane classification algorithm. The proposed algorithm is evaluated on our dataset that captures challenging road conditions. The proposed method performs better than the state-of-the-art method, showing 3% higher True Positive Rate and 2% lower False Positive Rate performance.


Curved multi-lane detection and tracking Feedback RANSAC Lane classification Local adaptive threshold Kalman filter 



This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018- 2016-0-00314) supervised by the IITP(Institute for Information & communications Technology Promotion). This research was partially supported by the Technology Innovation Program (No. 10083646, ‘Development of Deep Learning Based Future Prediction and Risk Assessment technology considering Inter-vehicular Interaction in Cut-in Scenario’) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).

Supplementary material

Supplementary material 1 (mp4 38530 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Cho Chun Shik Graduate School of Green TransportationKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea
  2. 2.Mechanical Technology Research CenterKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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