Lane Detection Based on Road Module and Extended Kalman Filter

  • Jinsheng XiaoEmail author
  • Li Luo
  • Yuan Yao
  • Wentao Zou
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)


Lane detection is already a basic component in modern vehicle control systems, with satisfying accuracy on labeled roads. There are still occasional problems of low accuracy and robustness in cases of challenging lighting, shadows, or in cases that road marking is missing. The paper proposes a new algorithm combining a model of road structure with an extended Kalman filter. Lane borders are detected by an adaptive edge detection operator based on scan lines. A new parameter space is defined to adjust the algorithm to the current lane model. All candidate lanes are extracted by voting of edge points. Road boundaries are obtained by considering various constraints. A new driveway model is specified according to roadway geometry and vehicle dynamics. The estimation of parameters is expanded for also covering driveway information. Coordinates of lane border points are tracked and estimated using an extended Kalman filter. Special attention is paid to enhancing stability and robustness of the algorithm. Results indicate that the proposed algorithm is robust under various lighting conditions and road scenarios; it is also of low computational complexity.



This work is supported by National Natural Science Foundation of China (Grant No. 61471272), Natural Science Foundation of Hubei Province, China (Grant No. 2016CFB499).


  1. 1.
    Hillel, A.B., Lerner, R., Levi, D., Raz, G.: Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25(3), 727–745 (2014)CrossRefGoogle Scholar
  2. 2.
    Huang, A.S., Moore, D., Antone, M., Olson, E., Teller, S.: Finding multiple lanes in urban road networks with vision and LIDAR. Auton. Robots 26, 103–122 (2009)CrossRefGoogle Scholar
  3. 3.
    Aly, M.: Real time detection of lane markers in urban streets. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 7–12 (2008)Google Scholar
  4. 4.
    Xiao, J.-S., Cheng, X., Li, B.-J., et al.: Lane detection algorithm based on beamlet transformation and k-means clustering. J. Sichuan Univ. (Eng. Sci. Ed.) 47(4), 98–103 (2015). (in Chinese)Google Scholar
  5. 5.
    Meng, L.-X., Sun, F.-C., Shao, Y.: Survey on road image interpretation based on monocular vision. J. Comput. Appl. 30(6), 1552–1555 (2010). (in Chinese)Google Scholar
  6. 6.
    Xu, H.-R., Wang, X.-D., Fang, Q.: Structure road detection algorithm based on B-spline curve model. Acta Automatica Sinica 37(3), 270–275 (2011). (in Chinese)CrossRefGoogle Scholar
  7. 7.
    Yu, B., Jain, A.K.: Lane boundary detection using a multiresolution Hough transform. In: Proceedings of IEEE International Conference on Image Processing, pp. 748–751 (1997)Google Scholar
  8. 8.
    Lee, J.W.: A machine vision system for lane departure detection. Comput. Vis. Image Underst. 86(1), 52–78 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kang, D.J., Jung, M.H.: Road lane segmentation using dynamic programming for active safety vehicles. Pattern Recogn. Lett. 24, 3177–3185 (2003)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Teoh, E., Shen, D.: Lane detection and tracking using B-Snake. Image Vis. Comput. 22, 269–280 (2004)CrossRefGoogle Scholar
  11. 11.
    Zhou, S., Jiang, Y.: A novel lane detection based on geometrical model and Gabor filter. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 59–64 (2010)Google Scholar
  12. 12.
    Mechat, N., Saadia, N., M’Sirdi, N.K., Djelal, N.: Lane detection and tracking by monocular vision system in road vehicle. In: Proceedings of IEEE International Conference on Image, Signal Processing, pp. 1276–1282 (2012)Google Scholar
  13. 13.
    Kortli, Y., Marzougui, M., Atri, M.: Efficient implementation of a real-time lane departure warning system. In: Proceedings of IEEE International Conference on Image Processing Applications Systems, pp. 1–6 (2016)Google Scholar
  14. 14.
    Shin, B., Tao, J., Klette, R.: A superparticle filter for lane detection. Pattern Recogn. 48, 3333–3345 (2014)CrossRefGoogle Scholar
  15. 15.
    Lee, D., Shin, J., Jung, J., et al.: Real-time lane detection and tracking system using simple filter and Kalman filter. In: Proceedings of IEEE International Conference Ubiquitous Future Networks, pp. 275–277 (2017)Google Scholar
  16. 16.
    Xu, Z., Sin, B., Klette, R.: Closed form line-segment extraction using the Hough transform. Pattern Recogn. 48, 4012–4023 (2015)CrossRefGoogle Scholar
  17. 17.
    Hough, P.V.C.: Method and means for recognizing complex patterns. US Patent, pp. 77–79 (1962)Google Scholar
  18. 18.
    Xiao, J., Liu, T., Zhang, Y., et al.: Multi-focus image fusion based on depth extraction with inhomogeneous diffusion equation. Signal Process. 125, 171–186 (2016)CrossRefGoogle Scholar
  19. 19.
    Dickmanns, E.D., Mysliwetz, B.D.: Recursive 3D road and relative ego-state recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 199–213 (1992)CrossRefGoogle Scholar
  20. 20.
    Watanabe, A., Naito, T., Ninomiya, Y.: Lane detection with roadside structure using on board monocular camera. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 191–196 (2009)Google Scholar
  21. 21.
    Welch, G., Bishop, G.: An introduction to the Kalman filter. UNC, Chapel Hill, TR (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronic InformationWuhan UniversityWuhanChina
  2. 2.Collaborative Innovation Center of Geospatial TechnologyWuhanChina
  3. 3.College of Physical Science and TechnologyCentral China Normal UniversityWuhanChina
  4. 4.School of Engineering, Computer, and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations