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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

Abstract

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.

Keywords

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

Notes

Acknowledgements

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)

References

  1. 1.
    Abramov, A., Bayer, C., Heller, C., Loy, C.: Multi-lane perception using feature fusion based on graphslam. In: Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pp. 3108–3115. IEEE (2016)Google Scholar
  2. 2.
    Aly, M.: Real time detection of lane markers in urban streets. In: Intelligent Vehicles Symposium, 2008 IEEE, pp. 7–12. IEEE (2008)Google Scholar
  3. 3.
    Bertozz, M., Broggi, A., Fascioli, A.: Stereo inverse perspective mapping: theory and applications. Image Vis. Comput. 16(8), 585–590 (1998)CrossRefGoogle Scholar
  4. 4.
    Bertozzi, M., Broggi, A.: Real-time lane and obstacle detection on the gold system. In: Intelligent Vehicles Symposium, 1996, Proceedings of the 1996 IEEE, pp. 213–218. IEEE (1996)Google Scholar
  5. 5.
    Bertozzi, M., Broggi, A.: Gold: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Process. 7(1), 62–81 (1998)CrossRefGoogle Scholar
  6. 6.
    Borkar, A., Hayes, M., Smith, M.T.: Robust lane detection and tracking with ransac and kalman filter. In: Image Processing (ICIP), 2009 16th IEEE International Conference on, pp. 3261–3264. IEEE (2009)Google Scholar
  7. 7.
    Borkar, A., Hayes, M., Smith, M.T.: A novel lane detection system with efficient ground truth generation. IEEE Trans. Intell. Transp. Syst. 13(1), 365–374 (2012)CrossRefGoogle Scholar
  8. 8.
    Borkar, A., Hayes, M., Smith, M.T., Pankanti, S.: A layered approach to robust lane detection at night. In: Computational Intelligence in Vehicles and Vehicular Systems, 2009. CIVVS’09. IEEE Workshop on, pp. 51–57. IEEE (2009)Google Scholar
  9. 9.
    Danescu, R., Sobol, S., Nedevschi, S., Graf, T.: Stereovision-based side lane and guardrail detection. In: Intelligent Transportation Systems Conference, 2006. ITSC’06. IEEE, pp. 1156–1161. IEEE (2006)Google Scholar
  10. 10.
    Deng, J., Han, Y.: A real-time system of lane detection and tracking based on optimized ransac b-spline fitting. In: Proceedings of the 2013 Research in Adaptive and Convergent Systems, pp. 157–164. ACM (2013)Google Scholar
  11. 11.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  12. 12.
    Guo, J., Wei, Z., Miao, D.: Lane detection method based on improved ransac algorithm. In: Autonomous Decentralized Systems (ISADS), 2015 IEEE Twelfth International Symposium on, pp. 285–288. IEEE (2015)Google Scholar
  13. 13.
    Kang, S.N., Lee, S., Hur, J., Seo, S.W.: Multi-lane detection based on accurate geometric lane estimation in highway scenarios. In: 2014 IEEE Intelligent Vehicles Symposium ProceedingsGoogle Scholar
  14. 14.
    Kim, Z.: Robust lane detection and tracking in challenging scenarios. IEEE Trans. Intell. Transp. Syst. 9(1), 16–26 (2008)CrossRefGoogle Scholar
  15. 15.
    Liatsis, P., Goulermas, J., Katsande, P.: A novel lane support framework for vision-based vehicle guidance. In: Industrial Technology, 2003 IEEE International Conference on, vol. 2, pp. 936–941. IEEE (2003)Google Scholar
  16. 16.
    McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans. Intell. Transp. Syst. 7(1), 20–37 (2006)CrossRefGoogle Scholar
  17. 17.
    Mingfang, D., Junzheng, W., Nan, L., Duoyang, L.: Shadow lane robust detection by image signal local reconstruction. Int. J. Signal Process. Image Process. Pattern Recognit. 9(3), 89–102 (2016)Google Scholar
  18. 18.
    Narote, S.P., Bhujbal, P.N., Narote, A.S., Dhane, D.M.: A review of recent advances in lane detection and departure warning system. Pattern Recognit. 73, 216–234 (2018)CrossRefGoogle Scholar
  19. 19.
    Ozgunalp, U., Dahnoun, N.: Robust lane detection and tracking based on novel feature extraction and lane categorization. In: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pp. 8129–8133. IEEE (2014)Google Scholar
  20. 20.
    Procházka, Z.: Road tracking method suitable for both unstructured and structured roads. Int. J. Adv. Robotic Syst. 10(3), 158 (2013)CrossRefGoogle Scholar
  21. 21.
    Satzoda, R.K., Trivedi, M.M.: Efficient lane and vehicle detection with integrated synergies (elvis). In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pp. 708–713. IEEE (2014)Google Scholar
  22. 22.
    Satzoda, R.K., Trivedi, M.M.: Drive analysis using vehicle dynamics and vision-based lane semantics. IEEE Trans. Intell. Transp. Syst. 16(1), 9–18 (2015)CrossRefGoogle Scholar
  23. 23.
    Schreiber, D., Alefs, B., Clabian, M.: Single camera lane detection and tracking. In: Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, pp. 302–307. IEEE (2005)Google Scholar
  24. 24.
    Sehestedt, S., Kodagoda, S., Alempijevic, A., Dissanayake, G.: Robust lane detection in urban environments. In: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, pp. 123–128. IEEE (2007)Google Scholar
  25. 25.
    Sivaraman, S., Trivedi, M.M.: Improved vision-based lane tracker performance using vehicle localization. In: Intelligent Vehicles Symposium (IV), 2010 IEEE, pp. 676–681. IEEE (2010)Google Scholar
  26. 26.
    Sivaraman, S., Trivedi, M.M.: Integrated lane and vehicle detection, localization, and tracking: a synergistic approach. IEEE Trans. Intell. Transp. Syst. 14(2), 906–917 (2013)CrossRefGoogle Scholar
  27. 27.
    Tan, H., Zhou, Y., Zhu, Y., Yao, D., Wang, J.: Improved river flow and random sample consensus for curve lane detection. Adv. Mech. Eng. 7(7), 1687814015593866 (2015)CrossRefGoogle Scholar
  28. 28.
    Veit, T., Tarel, J.P., Nicolle, P., Charbonnier, P.: Evaluation of road marking feature extraction. In: Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on, pp. 174–181. IEEE (2008)Google Scholar
  29. 29.
    Wang, Y., Dahnoun, N., Achim, A.: A novel system for robust lane detection and tracking. Signal Process. 92(2), 319–334 (2012)CrossRefGoogle Scholar
  30. 30.
    Yu, B., Zhang, W., Cai, Y.: A lane departure warning system based on machine vision. In: Computational Intelligence and Industrial Application, 2008. PACIIA’08. Pacific-Asia Workshop on, vol. 1, pp. 197–201. IEEE (2008)Google Scholar
  31. 31.
    Zhou, S., Jiang, Y., Xi, J., Gong, J., Xiong, G., Chen, H.: A novel lane detection based on geometrical model and gabor filter. In: Intelligent Vehicles Symposium (IV), 2010 IEEE, pp. 59–64. IEEE (2010)Google Scholar

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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