Advanced Microsystems for Automotive Applications 2009 pp 129-163

Part of the VDI-Buch book series (VDI-BUCH)

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Stereovision-Based Sensor for Intersection Assistance

  • Sergiu Nedevschi
  • Radu Danescu
  • Tiberiu Marita
  • Florin Oniga
  • Ciprian Pocol
  • Silviu Bota
  • Marc Michael Meinecke
  • Marian Andrzej Obojski

Abstract

The intersection scenario imposes radical changes in the physical setup and in the processing algorithms of a stereo sensor. Due to the need for a wider field of view, which comes with distortions and reduced depth accuracy, increased accuracy in calibration and dense stereo reconstruction is required. The stereo matching process has to be performed on rectified images, by a dedicated stereo board, to free processor time for the high-level algorithms. In order to cope with the complex nature of the intersection, the proposed solution perceives the environment in two modes: a structured approach, for the scenarios where the road geometry is estimated from lane delimiters, and an unstructured approach, where the road geometry is estimated from elevation maps. The structured mode provides the parameters of the lane, and the position, size, speed and class of the static and dynamic objects, while the unstructured mode provides an occupancy grid having the cells labeled as free space, obstacle areas, curbs and isles.

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References

  1. [1]
    S. Nedevschi, et al., “A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision”, Proceedings of 2007 IEEE Intelligent Vehicles Symposium (IV2007), June 13-15, 2006, Istanbul, Turkey, pp 276-283 (2007).Google Scholar
  2. [2]
    T. Dang, C. Hoffmann, “Stereo calibration in vehicles”, Proceedings of IEEE Intelligent Vehicles Symposium (IV2004), June 14-17, 2004, Parma, Italy, pp.268-272. (2004).Google Scholar
  3. [3]
    T. Dang, C. Hoffmann, and C. Stiller, “Self-calibration for Active Automotive Stereo Vision”, Proceedings of IEEE Intelligent Vehicles Symposium (IV2006), June 13-15, 2006, Tokyo, Japan, pp.364-369. (2006).Google Scholar
  4. [4]
    T. Marita, et al., “Camera Calibration Method for Far Range Stereovision Sensors Used in Vehicles”, Proceedings of IEEE Intelligent Vehicles Symposium (IV2006), June 2006, Tokyo, Japan, pp. 356-363. (2006).Google Scholar
  5. [5]
    S. Nedevschi, C. Vancea, T. Marita, T. Graf, “On-Line Calibration Method for Stereovision Systems Used in Far Range Detection Vehicle Applications”, IEEE Transactions on Intelligent Transportation Systems, vol.8, no. 4, pp. 651-660, 2007.CrossRefGoogle Scholar
  6. [6]
    T. Marita, et al., “Calibration Accuracy Assessment Methods for Stereovision Sensors Used in Vehicles”, in Proceedings of IEEE 3-rd International Conference on Intelligent Computer Communication and Processing (ICCP2007), 6-8 September 2007, Cluj-Napoca, Romania, pp. 111-118. (2007).Google Scholar
  7. [7]
    C. Vancea, S. Nedevschi, ”Analysis of different image rectification approaches for binocular stereovision systems”, in Proceedings of IEEE 2nd International Conference on Intelligent Computer Communication and Processing (ICCP 2006), vol. 1, September 1-2, 2006, Cluj-Napoca, Romania, pp. 135-142. (2006).Google Scholar
  8. [8]
    E.D. Dickmanns, B.D. Mysliwetz, “Recursive 3-d road and relative ego-state recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no.2, pp. 199-213, 1992.CrossRefGoogle Scholar
  9. [9]
    R. Aufrere, et al., ”A model-driven approach for real-time road recognition”, Machine Vision and Applications, vol 13, no. 2, pp. 95-107, 2001.CrossRefGoogle Scholar
  10. [10]
    S. Nedevschi, et al., “High accuracy stereo vision system for far distance obstacle detection”, in Proceedings of IEEE Intelligent Vehicles Symposium (IV 2004), June 2004, Parma, Italy, pp. 292-297. (2004).Google Scholar
  11. [11]
    R. Danescu, et al., “Lane Geometry Estimation in Urban Environments Using a Stereovision System”, in Proceedings of IEEE Intelligent Transportation Systems Conference (ITSC 2007), September 2007, Seattle, USA, pp. 271-276. (2007).Google Scholar
  12. [12]
    R. Labayrade, et al., “A Multi-Model Lane Detector that Handles Road Singularities”, in Proceedings of IEEE Intelligent Transportation Systems Conference, October 2006, Toronto, Canada, pp. 1143-1148. (2006).Google Scholar
  13. [13]
    M. Isard, A. Blake, “CONDENSATION – conditional density propagation for visual tracking”, International Journal of Computer Vision, vol. 29, nr. 1, pp. 5-28, 1998CrossRefGoogle Scholar
  14. [14]
    B. Southall, C.J. Taylor, “Stochastic road shape estimation”, in Proceedings of IEEE International Conference on Computer Vision, 2001, Vancouver, Canada, pp. 205-212.Google Scholar
  15. [15]
    K. Macek, et al., “A Lane Detection Vision Module for Driver Assistance”, in Proceedings of IEEE/APS Conference on Mechatronics and Robotics, 2004.Google Scholar
  16. [16]
    U. Franke, et al., ”Lane Recognition on Country Roads“, in Proceedings of IEEE Intelligent Vehicles Symposium, June 2007, Istanbul, Turkey, pp.100-104. (2007).Google Scholar
  17. [17]
    P. Smuda, et al., ”Multiple Cue Data Fusion with Particle Filters for Road Course Detection in Vision Systems“, in Proceedings of IEEE Intelligent Vehicles Symposium, 2006, Tokyo, Japan, pp. 400-405. (2006).Google Scholar
  18. [18]
    R. Danescu, et al., “A Stereovision-Based Probabilistic Lane Tracker for Difficult Road Scenarios”, in Proceedings of IEEE Intelligent Vehicles Symposium 2008 (IV2008), Eindhoven, The Netherlands, June 4-6, 2008, pp.536-541. (2008).Google Scholar
  19. [19]
    D. M. Gavrila, “Pedestrian detection from a moving vehicle,” in Proceedings of the European Conference on Computer Vision, 2000,pp. 37–49.Google Scholar
  20. [20]
    A. Khammari, et al., “Vehicle detection combining gradient analysis and AdaBoost classification”, in Proceedings of Intelligent Transportation Systems Conference (ITSC 2005), pp. 61-71, 2005. (2005).Google Scholar
  21. [21]
    D. Huber, et al., “Parts-based 3D object classification”, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2004, vol 2, pp. 82-89. (2004).Google Scholar
  22. [22]
    R. Osada, et al., “Matching 3D Models with Shape Distributions”, Shape Modeling International, Genova, Italy, May 2001.Google Scholar
  23. [23]
    D. M. Gavrila, “A Bayesian Exemplar-based Approach to Hierarchical Shape Matching”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28 No. 8, pp. 1408-1421, 2007.CrossRefGoogle Scholar
  24. [24]
    L. Havasi, et al., “Pedestrian Detection Using Derived Third-Order Symmetry of Legs”, in Proc. of the Int. Conference on Computer Vision and Graphics, 2004, pp. 733-739. (2004).Google Scholar
  25. [25]
    A. Shashua, et al., “Pedestrian Detection for Driving Assistance Systems: Singleframe Classification and System Level Performance”, in Proceedings of IEEE Intelligent Vehicle Symposium(IV2004), June 2004, Parma, Italy, pp. 1-6. (2004).Google Scholar
  26. [26]
    M. Bertozzi, et al., “Stereo Vision-based approaches for Pedestrian Detection”, in Proceedings of the IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition - Workshops, June 2005, San Diego, USA, vol. 3, pp. 16, 2005.Google Scholar
  27. [27]
    Z. Zhang, “A stereovision system for a planetary rover: Calibration, correlation, registration, and fusion,” in Machine Vision and Applications, vol. 10, no. 1, pp. 27-34, 1997.CrossRefGoogle Scholar
  28. [28]
    M. Vergauwen, et al., “A stereo-vision system for support of planetary surface exploration”, Machine Vision and Applications, vol. 14, no.1, pp. 5–14, 2003.CrossRefGoogle Scholar
  29. [29]
    F. Oniga, et al.,, “Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo”, in Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems (ITSC 2007), Sept. 30 - Oct. 3, 2007, Seattle, Washington, USA, pp. 859-865. (2007).Google Scholar
  30. [30]
    F. Oniga, et al., “Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios”, in Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC 2008), Oct. 2008, Beijing, China, pp. 67-72. (2008).Google Scholar
  31. [31]
    J.Y. Bouguet, Camera Calibration Toolbox for Matlab, www.vision.caltech.edu/ bougetjGoogle Scholar
  32. [32]
    C. Pocol, et al., “Obstacle Detection Based on Dense Stereovision for Urban ACC Systems”, in Proceedings of 5th International Workshop on Intelligent Transportation (WIT 2008), March 18-19, 2008, Hamburg, Germany, pp. 13-18.Google Scholar
  33. [33]
    R. Danescu, et al., “Stereovision Based Vehicle Tracking in Urban Traffic Environments”, in Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC 2007), October 2007, Seattle, USA, pp.400-404. (2007).Google Scholar
  34. [34]
    S. Bota, S. Nedevschi - Multi-Feature Walking Pedestrians Detection for Driving Assistance Systems, IET Inteligent Transportation Systems Journal, Volume 2, Issue 2, pp. 92-104, 2008.CrossRefGoogle Scholar
  35. [35]
    J.-Y. Bouguet, “Pyramidal implementation of the Lucas Kanade feature tracker”, available: http://mrl.nyu.edu/ bregler/classes/vision spring06/bouget00.pdf.Google Scholar
  36. [36]
    N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), June 2005, pp. 886–893. (2005).Google Scholar
  37. [37]
    I. H. Witten, E. Frank “Data Mining: Practical machine learning tools and techniques, 2nd Edition”, Morgan Kaufmann, San Francisco, 2007.Google Scholar
  38. [38]
    S. Nedevschi, et al., "Forward Collision Detection using a Stereo Vision System", Proceedings of IEEE 4th International Conference on Intelligent Computer Communication and Processing (ICCP 2008), August 28-30, 2008, Cluj-Napoca, Romania, pp. 115-122. (2008).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sergiu Nedevschi
    • 1
  • Radu Danescu
    • 1
  • Tiberiu Marita
    • 1
  • Florin Oniga
    • 1
  • Ciprian Pocol
    • 1
  • Silviu Bota
    • 1
  • Marc Michael Meinecke
    • 2
  • Marian Andrzej Obojski
    • 2
  1. 1.Technical University of Cluj-NapocaCluj-NapocaRomania
  2. 2.Volkswagen AGWolfsburgGermany

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