Machine Vision and Applications

, Volume 27, Issue 2, pp 175–191 | Cite as

Vision-based approach towards lane line detection and vehicle localization

  • Xinxin DuEmail author
  • Kok Kiong Tan
Original Paper


Localization of the vehicle with respect to road lanes plays a critical role in the advances of making the vehicle fully autonomous. Vision based road lane line detection provides a feasible and low cost solution as the vehicle pose can be derived from the detection. While good progress has been made, the road lane line detection has remained an open one, given challenging road appearances with shadows, varying lighting conditions, worn-out lane lines etc. In this paper, we propose a more robust vision-based approach with respect to these challenges. The approach incorporates four key steps. Lane line pixels are first pooled with a ridge detector. An effective noise filtering mechanism will next remove noise pixels to a large extent. A modified version of sequential RANdom Sample Consensus) is then adopted in a model fitting procedure to ensure each lane line in the image is captured correctly. Finally, if lane lines on both sides of the road exist, a parallelism reinforcement technique is imposed to improve the model accuracy. The results obtained show that the proposed approach is able to detect the lane lines accurately and at a high success rate compared to current approaches. The model derived from the lane line detection is capable of generating precise and consistent vehicle localization information with respect to road lane lines, including road geometry, vehicle position and orientation.


Lane line detection and tracking Vehicle localization  Road shape modelling and interpretation Sequential RANSAC 

Supplementary material

Supplementary material 1 (mp4 5717 KB)

Supplementary material 2 (mp4 5705 KB)


  1. 1.
    Aufrere, R., Chapuis, R., Chausse, F.: A model-driven approach for real-time road recognition. Mach. Vis. Appl. 13(2), 95–107 (2001)CrossRefGoogle Scholar
  2. 2.
    Borkar, A., Hayes, M., Smith, M.T.: A novel lane detection system with efficient ground truth generation. Intell. Transp. Syst. IEEE Trans. 13(1), 365–374 (2012)CrossRefGoogle Scholar
  3. 3.
    Bouguet, J.Y.: Camera calibration toolbox for matlab. (2010-07).
  4. 4.
    Danescu, R., Nedevschi, S.: Probabilistic lane tracking in difficult road scenarios using stereovision. Intell. Transp. Syst. IEEE Trans. 10(2), 272–282 (2009)CrossRefGoogle Scholar
  5. 5.
    David, F., Scharstein, D.: Multi-model estimation in the presence of outliers. Master’s thesis, Middlebury College (2011)Google Scholar
  6. 6.
    Fritsch, J., Kuhnl, T., Kummert, F.: Monocular road terrain detection by combining visual and spatial information. Intell. Transp. Syst. IEEE Trans. 15(4), 1586–1596 (2014)CrossRefGoogle Scholar
  7. 7.
    Gopalan, R., Hong, T., Shneier, M., Chellappa, R.: A learning approach towards detection and tracking of lane markings. Intell. Transp. Syst. IEEE Trans. 13(3), 1088–1098 (2012)CrossRefGoogle Scholar
  8. 8.
    Guiducci, A.: Parametric model of the perspective projection of a road with applications to lane keeping and 3d road reconstruction. Comput. Vis. Image Underst. 73(3), 414–427 (1999)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gyory, G.: Obstacle detection methods for stereo vision as driving aid. In: Advanced Robotics, 2003. International Conference on, IEEE, pp. 477–481 (2003)Google Scholar
  10. 10.
    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
  11. 11.
    Kang, D.J., Jung, M.H.: Road lane segmentation using dynamic programming for active safety vehicles. Pattern Recogn. Lett. 24(16), 3177–3185 (2003)CrossRefGoogle Scholar
  12. 12.
    Kim, Z.: Robust lane detection and tracking in challenging scenarios. Intell. Transp. Syst. IEEE Trans. 9(1), 16–26 (2008)CrossRefGoogle Scholar
  13. 13.
    Li, H., Nashashibi, F.: Robust real-time lane detection based on lane mark segment features and general a priori knowledge. In: Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on, IEEE, pp. 812–817 (2011)Google Scholar
  14. 14.
    Li, Q., Zheng, N., Cheng, H.: An adaptive approach to lane markings detection. In: Intelligent transportation systems, 2003. Proceedings., IEEE, vol. 1, pp. 510–514 (2003)Google Scholar
  15. 15.
    Liu, W., Zhang, H., Duan, B., Yuan, H., Zhao, H.: Vision-based real-time lane marking detection and tracking. In: Intelligent transportation systems, 2008. ITSC 2008. 11th International IEEE Conference on, IEEE, pp. 49–54 (2008)Google Scholar
  16. 16.
    López, A., Serrat, J., Canero, C., Lumbreras, F., Graf, T.: Robust lane markings detection and road geometry computation. Int. J. Autom. Technol. 11(3), 395–407 (2010)CrossRefGoogle Scholar
  17. 17.
    McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. Intell. Transp. Syst. IEEE Trans. 7(1), 20–37 (2006)CrossRefGoogle Scholar
  18. 18.
    Nedevschi, S., Schmidt, R., Graf, T., Danescu, R., Frentiu, D., Marita, T., Oniga, F., Pocol, C.: 3d lane detection system based on stereovision. In: Intelligent transportation systems, 2004. Proceedings. The 7th International IEEE Conference on, IEEE, pp. 161–166 (2004)Google Scholar
  19. 19.
    Press, W.H.: Numerical recipes in Fortran 77: the art of scientific computing, vol. 1. Cambridge University Press (1992)Google Scholar
  20. 20.
    Sampson, P.D.: Fitting conic sections to very scattered data: an iterative refinement of the bookstein algorithm. Comput. Graph. Image Process. 18(1), 97–108 (1982)CrossRefGoogle Scholar
  21. 21.
    Sivaraman, S., Trivedi, M.M.: Integrated lane and vehicle detection, localization, and tracking: a synergistic approach. Intell. Transp. Syst. IEEE Trans. 14(2), 906–917 (2013)CrossRefGoogle Scholar
  22. 22.
    Sun, T.Y., Tsai, S.J., Chan, V.: Hsi color model based lane-marking detection. In: Intelligent transportation systems conference, ITSC’06. IEEE, pp. 1168–1172 (2006)Google Scholar
  23. 23.
    Tapia-Espinoza, R., Torres-Torriti, M.: A comparison of gradient versus color and texture analysis for lane detection and tracking. In: Robotics symposium (LARS), 2009 6th Latin American, IEEE, pp. 1–6 (2009)Google Scholar
  24. 24.
    Thorpe, C., Hebert, M.H., Kanade, T., Shafer, S.A.: Vision and navigation for the carnegie-mellon navlab. Pattern Anal. Mach. Intell. IEEE Trans. 10(3), 362–373 (1988)CrossRefGoogle Scholar
  25. 25.
    Wang, J., Schroedl, S., Mezger, K., Ortloff, R., Joos, A., Passegger, T.: Lane keeping based on location technology. Intell. Transp. Syst. IEEE Trans. 6(3), 351–356 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

Personalised recommendations