Applications of Computer Vision to Vehicles: An Extreme Test

  • Alberto Broggi
  • Stefano Cattani
  • Paolo Medici
  • Paolo Zani
Part of the Studies in Computational Intelligence book series (SCI, volume 411)


VisLab has been pioneering the world of autonomous driving since its early years; in 1998 VisLab organized one of the most innovative experiments for that period: a passenger car was equipped with sensing and actuation devices and was tested with autonomous steering along a 2000+ km on Italian highways [5].

VisLab then contiuned its efforts within this very promising research domain; it partnered with different companies and implemented the perception system of TerraMax, the largest entry in the DARPA Grand Challenge. In 2005 TerraMax was one of only 5 vehicles to successdully finish the race: about 220 km in autonomous mode along the Mohave desert in Nevada [4].


Autonomous Mode Obstacle Detection Target Vehicle Lane Marking 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bertozzi, M., Broggi, A., Fascioli, A.: Stereo Inverse Perspective Mapping: Theory and Applications. Image and Vision Computing Journal 8(16), 585–590 (1998)CrossRefGoogle Scholar
  2. 2.
    Bombini, L., Cattani, S., Cerri, P., Fedriga, R.I., Felisa, M., Porta, P.P.: Test bed for Unified Perception & Decision Architecture. In: Procs. 13th Int. Forum on Advanced Microsystems for Automotive Applications, Berlin, Germany (May 2009)Google Scholar
  3. 3.
    Braid, D., Broggi, A., Schmiedel, G.: The TerraMax Autonomous Vehicle. Journal of Field Robotics 23(9), 693–708 (2006)CrossRefGoogle Scholar
  4. 4.
    Braid, D., Broggi, A., Schmiedel, G.: The TerraMax Autonomous Vehicle concludes the 2005 DARPA Grand Challenge. In: Procs. IEEE Intelligent Vehicles Symposium 2006, Tokyo, Japan, pp. 534–539 (June 2006)Google Scholar
  5. 5.
    Broggi, A., Bertozzi, M., Fascioli, A., Conte, G.: Automatic Vehicle Guidance: the Experience of the ARGO Vehicle. World Scientific, Singapore (1999) ISBN 9810237200zbMATHCrossRefGoogle Scholar
  6. 6.
    Broggi, A., Cappalunga, A., Caraffi, C., Cattani, S., Ghidoni, S., Grisleri, P., Porta, P.P., Posterli, M., Zani, P.: TerraMax Vision at the Urban Challenge 2007. IEEE Trans. on Intelligent Transportation Systems 11(1), 194–205 (2010)CrossRefGoogle Scholar
  7. 7.
    Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P., Jung, H.G.: A New Approach to Urban Pedestrian Detection for Automatic Braking. IEEE Trans. on Intelligent Transportation Systems 10(4), 594–605 (2009) ISSN: 1524-9050CrossRefGoogle Scholar
  8. 8.
    Chen, Y.-L., Sundareswaran, V., Anderson, C., Broggi, A., Grisleri, P., Porta, P.P., Zani, P., Beck, J.: TerraMax: Team Oshkosh Urban Robot. Journal of Field Robotics 25(10), 841–860 (2008)CrossRefGoogle Scholar
  9. 9.
    Chen, Y.-L., Sundareswaran, V., Anderson, C., Broggi, A., Grisleri, P., Porta, P.P., Zani, P., Beck, J.: TerraMax: Team Oshkosh Urban Robot. In: Buehler, M., Iagnemma, K., Singh, S. (eds.) The DARPA Urban Challenge, Autonomous Vehicles in City Traffic. Springer Tracts in Advanced Robotics, pp. 595–622. Springer, Heidelberg (2009) ISBN: 978-3-642-03990-4Google Scholar
  10. 10.
    Felisa, M., Zani, P.: Incremental Disparity Space Image computation for automotive applications. In: Procs. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, St.Louis, Missouri, USA (October 2009)Google Scholar
  11. 11.
    Frchet, M.M.: Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo (1884-1940) 22(1), 132–136 (1906)Google Scholar
  12. 12.
    Gehrig, S., Rabe, C.: Real-time semi-global matching on the cpu. In: ECVW 2010, pp. 85–92 (2010)Google Scholar
  13. 13.
    Hakimi, S.L., Schmeichel, E.F.: Fitting polygonal functions to a set of points in the plane. CVGIP: Graph. Models Image Process. 53(2), 132–136 (1991)zbMATHCrossRefGoogle Scholar
  14. 14.
    Hangouet, J.F.: Computation of the Hausdorff distance between plane vector polylines. In: Procs. Twelfth International Symposium on Computer-Assisted Cartography, Charlotte, Noth Carolina, USA, vol. 4, pp. 1–10 (1995)Google Scholar
  15. 15.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518Google Scholar
  16. 16.
    Hirschmüller, H.: Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information. In: Intl. Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, vol, June 2005, vol. 2, pp. 807–814. IEEE Computer Society Press, San Diego (2005)Google Scholar
  17. 17.
    Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. PAMI 31(9), 1582–1599 (2009)CrossRefGoogle Scholar
  18. 18.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, vol. 2, pp. 674–679. Morgan Kaufmann Publishers Inc., San Francisco (1981)Google Scholar
  19. 19.
    Mallot, H.A., Bülthoff, H.H., Little, J.J., Bohrer, S.: Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biological Cybernetics 64, 177–185 (1991)zbMATHCrossRefGoogle Scholar
  20. 20.
    Manduchi, R., Castano, A., Talukder, A., Matthies, L.: Obstacle detection and terrain classification for autonomous off-road navigation. Auton. Robots 18(1), 81–102 (2005)CrossRefGoogle Scholar
  21. 21.
    McMaster, R.B.: A statistical analysis of mathematical measures for linear simplification. Cartography and Geographic Information Science 13(2), 103–116 (1986)CrossRefGoogle Scholar
  22. 22.
    Nedevschi, S., Danescu, R., Schmidt, R., Graf, T.: High accuracy stereovision system for far distance obstacle detection. In: Procs. IEEE Intelligent Vehicles Symposium 2004, Parma, Italy (June 2004)Google Scholar
  23. 23.
    Nedevschi, S., Oniga, F., Danescu, R., Graf, T., Schmidt, R.: Increased Accuracy Stereo Approach for 3D Lane Detection. In: IEEE Intelligent Vehicles Symposium, Tokyo, Japan, pp. 42–49 (June 2006)Google Scholar
  24. 24.
    Peuquet, D.J.: An algorithm for calculating minimum euclidean distance between two geographic features. Computers & Geosciences 18(8), 989–1001 (1992)CrossRefGoogle Scholar
  25. 25.
    Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (January 1994)Google Scholar
  26. 26.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38 (December 2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Alberto Broggi
    • 1
  • Stefano Cattani
    • 1
  • Paolo Medici
    • 1
  • Paolo Zani
    • 1
  1. - Artificial Vision and Intelligent Systems LabParmaIT

Personalised recommendations