A Robust Lane Detection Approach Based on MAP Estimate and Particle Swarm Optimization

  • Yong Zhou
  • Xiaofeng Hu
  • Qingtai Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3802)

Abstract

In this paper, a robust lane detection approach, that is primary and essential for driver assistance systems, is proposed to handle the situations where the lane boundaries in an image have relatively weak local contrast, or where there are strong distracting edges. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori (MAP) formulation of the lane detection problem, and a particle swarm optimization algorithm to maximize the posterior density. The model parameters completely determine the position of the vehicle inside the lane, its heading direction, and the local structure of the lane. Experimental results reveal that the proposed method is robust against noise and shadows in the captured road images.

References

  1. 1.
    Bcher, T., Curio, C., Edelbrunner, J., Igel, C., Kastrup, D., et al.: Image processing and behavior planning for intelligent vehicles. IEEE Transactions on Industrial Electronics 50(1), 62–75 (2003)CrossRefGoogle Scholar
  2. 2.
    Li, Q., Zheng, N., Cheng, H.: Springrobot: a prototype autonomous vehicle and its algorithms for lane detection. IEEE Transactions on Intelligent Transportation Systems 5(4), 300–308 (2004)CrossRefGoogle Scholar
  3. 3.
    Wang, Y., Teoh, E.K., Shen, D.: Lane detection using B-Snake. Image and Vision Computing 22(4), 269–280 (2004)CrossRefGoogle Scholar
  4. 4.
    Chapuis, R., Aufrere, R., Chausse, F.: Accurate road following and reconstruction by computer vision. IEEE Transactions on Intelligent transportation systems 3(4), 261–270 (2002)CrossRefGoogle Scholar
  5. 5.
    Park, J.W., Lee, J.W., Jhang, K.Y.: A lane-curve detection based on an LCF. Pattern Recognition Letters 24(14), 2301–2313 (2003)CrossRefGoogle Scholar
  6. 6.
    Kluge, K., Lakshmanan, S.: A deformable-template approach to lane detection. In: Proc. IEEE Intelligent Vehicles Symposium, pp. 54–59 (1995)Google Scholar
  7. 7.
    Dickmanns, E.D., Mysliwetz, B.D.: Recursive 3-D road and relative ego-state recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 199–213 (1992)CrossRefGoogle Scholar
  8. 8.
    Guiducci, A.: Parametric Model of the perspective projective projection of a road with application to lane keeping and 3D road reconstruction. Computer Vision and Image Understanding 73(3), 414–427 (1999)MATHCrossRefGoogle Scholar
  9. 9.
    Talbi, E.-G., Muntean, T.: Hill-climbing, simulated annealing and genetic algorithm: a comparative study and application to the mapping problem. In: Proc. of the Twenty-Sixth Hawaii International Conference on System Sciences, vol. 2, pp. 565–573 (1993)Google Scholar
  10. 10.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symp. Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  11. 11.
    Engelbrecht, A.P., Ismail, A.: Training product unit neural networks. Stability Control: Theory Appl. 2(1-2), 59–74 (1999)Google Scholar
  12. 12.
    Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. Congr. Evolutionary Computation, Washington, DC, July 1999, pp. 1945–1949 (1999)Google Scholar
  13. 13.
    Robinson, J., Rahmat-Samii, Y.: Particle swarm optimization in electromagnetics. IEEE Trans. Antennas Propag. 52(2), 397–407 (2004)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Huang, T., Sanagavarapu, A.: A hybrid boundary condition for robust particle swarm optimization. IEEE Antennas and Wireless Propagation Letters 4(1) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yong Zhou
    • 1
  • Xiaofeng Hu
    • 1
  • Qingtai Ye
    • 1
  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations