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Computational Intelligence in Automotive Applications

  • Yifei Wang
  • Naim Dahnoun
  • Alin Achim
Part of the Studies in Computational Intelligence book series (SCI, volume 410)

Abstract

In this chapter, we discuss one of the most popular machine vision applications in the automotive industry: lane detection and tracking. Model-based lane detection algorithms can be separated into lane modeling, feature extraction and model parameter estimation. Each of these steps is discussed in detail with examples and results. A recently proposed lane feature extraction approach, which is called the Global Lane Feature Refinement Algorithm (GLFRA), is also introduced. It provides a generalized framework to significantly improve various types of gradient-based lane feature maps by utilizing the global shape information and subsequently improves the parameter estimation and the tracking performance. Another important aspect of this application lies in the tracking stage.We compare the performances of three different types of particle filters (the sampling importance resampling particle filter, the Gaussian particle filter and the Gaussian sum particles filter) quantitatively and provide insightful result analysis and suggestions. Furthermore, the influence of featuremaps on the tracking performance is also investigated.

Keywords

Feature Point Automotive Application Intelligent Transportation System Posterior Probability Density Lane Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Beichl, I., Sullivan, F.: The Metropolis algorithm. Computing in Science and Engineering 2, 65–69 (2000)CrossRefGoogle Scholar
  2. 2.
    Benmansour, N., Labayrade, R., Aubert, D., Glaser, S.: Stereovision-based 3D lane detection system: a model driven approach. In: Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (2008)Google Scholar
  3. 3.
    Bertozzi, M., Broggi, A.: GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Transactions on Image Processing 7, 62–81 (1998)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), doi:10.1109/TITS.2002.804751CrossRefGoogle Scholar
  5. 5.
    Cheng, H.Y., Jeng, B.S., Tseng, P.T., Fan, K.C.: Lane detection with moving vehicles in the traffic scenes. IEEE Transactions on Intelligent Transportation Systems 7(4), 571–582 (2006), doi:10.1109/TITS.2006.883940CrossRefGoogle Scholar
  6. 6.
    Dahnoun, N.: Digital Signal Processing Implementation: Using the TMS320C6000 Processors. Prentice-Hall PTR (2000)Google Scholar
  7. 7.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from in-complete data via the EM algorithm. Journal of the Royal Statistical Society: Series B 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Dickmanns, E.D., Mysliwetz, B.D.: Recursive 3-d road and relative ego-state recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14, 199–213 (1992), http://portal.acm.org/citation.cfm?id=132013.132020, doi:10.1109/34.121789CrossRefGoogle Scholar
  9. 9.
    Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer (2001)Google Scholar
  10. 10.
    He, Y., Wang, H., Zhang, B.: Color-based road detection in urban traffic scenes. IEEE Transactions on Intelligent Transportation Systems 5, 309–317 (2004)CrossRefGoogle Scholar
  11. 11.
    Ieng, S.S., Tarel, J.P., Labayrade, R.: On the design of a single lane-markings detectors regardless the on-board camera’s position. In: Proceedings of IEEE Intelligent Vehicles Symposium 2003, pp. 564–569. IEEE (2003)Google Scholar
  12. 12.
    Kang, D.J., Choi, J.W., Kweon, L.S.: Finding and tracking road lanes using line-snakes. In: Proceedings of Conference on Intelligent Vehicle (1996)Google Scholar
  13. 13.
    Kang, D.-J., Jung, M.-H.: Road lane segmentation using dynamic programming for active safety vehicles. Pattern Recognition Letters 24, 3177–3185 (2003)CrossRefGoogle Scholar
  14. 14.
    Kluge, K., Lakshmanan, S.: A deformable-template approach to lane detection. In: Proceedings of the Intelligent Vehicles 1995 Symposium, pp. 54–59 (1995)Google Scholar
  15. 15.
    Kotecha, J.H., Djuric, P.M.: Gaussian particle filtering. IEEE Transactions on Signal Processing 51, 2592–2601 (2003)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kotecha, J.H., Djuric, P.M.: Gaussian sum particle filtering. IEEE Transactions on Signal Processing 51, 2602–2612 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kreucher, C., Lakshmanan, S.: LANA: a lane extraction algorithm that uses frequency domain features. IEEE Transactions on Robotics and Automation 15, 343–350 (1995)CrossRefGoogle Scholar
  18. 18.
    Li, Q., Zheng, N., Cheng, H.: Springrobot: A prototype autonomous vehicle and its algorithms for lane detection. IEEE Transactions on Intelligent Transportation Systems 5, 300–308 (2004)CrossRefGoogle Scholar
  19. 19.
    Lim, K.H., Seng, K.P., Ang, L.-M., Chin, S.W.: Lane detection and Kalman-based linear-parabolic lane tracking. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009, vol. 2, pp. 351–354 (2009), doi:10.1109/IHMSC.2009.211Google Scholar
  20. 20.
    McCall, J.C., Trivedi, M.M.: An integrated, robust approach to lane marking detection and lane tracking. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 533–537 (2004)Google Scholar
  21. 21.
    McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation. IEEE Transactions on Intelligent Transportation Systems 7, 20–37 (2006)CrossRefGoogle Scholar
  22. 22.
    Mihaylova, L., Brasnett, P., Achim, A., Bull, D., Canagarajah, N.: Particle filtering with alpha-stable distributions. In: Proceedings of the 13th IEEE Statistical Signal Processing Workshop (SSP), pp. 381–386 (2005)Google Scholar
  23. 23.
    Nedevschi, S., Schmidt, R., Graf, T., Danescu, R.: 3D lane detection system based on stereo vision. In: IEEE Intelligent Transportation Systems Conference (2004)Google Scholar
  24. 24.
    Park, J.W., Lee, J.W., Jhang, K.Y.: A lane-curve detection based on an lcf. Pattern Recognition Letters 24, 2301–2313 (2003)CrossRefGoogle Scholar
  25. 25.
    Pomerleau, D.: RALPH: rapidly adapting lateral position handler. In: Proceedings of the IEEE Symposium on Intelligent Vehicles, pp. 506–511 (1995)Google Scholar
  26. 26.
    Shufelt, J.A.: Performance evaluation and analysis of vanishing point detection techniques. IEEE Pattern Analysis and Machine Intelligence 21, 282–288 (1999)CrossRefGoogle Scholar
  27. 27.
    Veit, T., Tarel, J.P., Nicolle, P., Charbonnier, P.: Evaluation of road marking feature extraction. In: 11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008, pp. 174–181 (2008)Google Scholar
  28. 28.
    Wang, Y., Bai, L., Michael, F.: Robust road modeling and tracking using condenzation. IEEE Transactions on Intelligent Transportation Systems 9, 570–579 (2008)CrossRefGoogle Scholar
  29. 29.
    Wang, Y., Dahnoun, N., Achim, A.: A novel lane feature extraction algorithm implemented on the TMS320DM6437 DSP platform. In: Proceedings of the 16th International Conference on Digital Signal Processing, pp. 733–738 (2009)Google Scholar
  30. 30.
    Wang, Y., Shen, D., Teoh, E.K.: Lane detection using spline model. Pattern Recognition Letters 21(8), 677–689 (2000), doi:10.1016/S0167-8655(00)00021-0CrossRefGoogle Scholar
  31. 31.
    Wang, Y., Shen, D., Teoh, E.K.: Lane detection using spline model. Pattern Recognition Letters 21, 677–689 (2000)CrossRefGoogle Scholar
  32. 32.
    Wang, Y., Teoh, E.K., Shen, D.: Lane detection and tracking using B-snake. Image and Vision Computing 22, 269–280 (2004)CrossRefGoogle Scholar
  33. 33.
    Yim, Y., Oh, S.-Y.: Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving. In: Proceedings of International Conference on Intelligent Transportation Systems, pp. 929–932 (1999)Google Scholar
  34. 34.
    Zhang, J., Nagel, H.-H.: Texture-based segmentation of road images. In: Proceedings of the Intelligent Vehicles 1994 Symposium, pp. 260–265 (1994)Google Scholar
  35. 35.
    Zhou, Y., Xu, R., Hu, X., Ye, Q.: A robust lane detection and tracking method based on computer vision. Measurement Science and Technology 17, 736–745 (2006), doi:10.1088/0957-0233/17/4/020Google Scholar

Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Visual Information Lab, Department of Electrical & Electronic EngineeringUniversity of BristolBristolUK

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