Perception Tasks: Lane Detection

  • Luca Mazzei
  • Paolo Zani
Reference work entry


The localization of painted road markings is a key aspect of environment reconstruction in urban, rural, and highway areas, allowing a precise definition of the safely drivable area in front of the vehicle.

Lane detection algorithms are largely exploited by active safety systems in the automotive field, with the aim of warning the driver against unintended road departures, but are also essential in fully autonomous vehicles, since they complement the data coming from other sources, like digital maps, making it possible to navigate precisely even in complex scenarios.

This chapter introduces potential approaches and requirements for lane detection and describes in detail one of such algorithms and its results.


Inertial Measurement Unit Autonomous Vehicle Lane Change 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.


  1. Bertozzi M, Broggi A, Fascioli A (1998) Stereo inverse perspective mapping: theory and applications. Image Vis Comput J 8(16):585–590CrossRefGoogle Scholar
  2. Bertozzi M, Bombini L, Broggi A, Buzzoni M, Cardarelli E, Cattani S, Cerri P, Debattisti S, Fedriga RI, Felisa M, Gatti L, Giacomazzo A, Grisleri P, Laghi MC, Mazzei L, Medici P, Panciroli M, Porta PP, Zani P (2010) The VisLab intercontinental autonomous challenge: 13,000 km, 3 months, no driver. In: Proceedings of the 17th World congress on ITS, Busan, South Korea, Oct 2010Google Scholar
  3. Bin Zhang W, Parsons R (1994) Intelligent roadway reference system for vehicle lateral guidance and control, United States Patent 5347456, September 1994Google Scholar
  4. Broggi A, Cappalunga A, Caraffi C, Cattani S, Ghidoni S, Grisleri P, Porta PP, Posterli M, Zani P (2010) TerraMax vision at the urban challenge 2007. IEEE Trans Intell Trans Syst 11(1):194–205CrossRefGoogle Scholar
  5. Buehler M, Iagnemma K, Singh S (2009) The DARPA urban challenge: autonomous vehicles in city traffic. In: The proceedings of the DARPA Urban Challenge, Seattle, 2009Google Scholar
  6. Frchet MM (1906) Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo (1884–1940), 22(1):132–136Google Scholar
  7. Hakimi SL, Schmeichel EF (1991) Fitting polygonal functions to a set of points in the plane. CVGIP: Graph Models Image Process 53(2):132–136MATHCrossRefGoogle Scholar
  8. Hangouet JF (1995) Computation of the Hausdorff distance between plane vector polylines. In: Proceedings of the twelfth international symposium on computer-assisted cartography, vol 4, Charlotte, North Carolina, USA, pp 1–10Google Scholar
  9. Mallot HA, Bülthoff HH, Little JJ, Bohrer S (1991) Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biol Cyberne 64:177–185MATHCrossRefGoogle Scholar
  10. McMaster RB (1986) A statistical analysis of mathematical measures for linear simplification. Cartograph Geograph Inform Sci 13(2):103–116CrossRefGoogle Scholar
  11. Nedevschi S, Oniga F, Danescu R, Graf T, Schmidt R (2006) Increased accuracy stereo approach for 3D lane detection. In: IEEE intelligent vehicles symposium, Tokyo, Japan, pp 42–49, June 2006Google Scholar
  12. Peuquet DJ (1992) An algorithm for calculating minimum euclidean distance between two geographic features. J Comput Geosci 18(8):989–1001CrossRefGoogle Scholar
  13. Shladover SE (2007) Lane assist systems for bus rapid transit, Volume I: Technology Assessment. California path research report UCB-ITS-PRR-2007-21Google Scholar
  14. VisLab (2010) VIAC web site.
  15. Zhang W-B (1991) A roadway information system for vehicle guidance/control. In: Vehicle navigation and information systems conference 1991, vol 2, Dearborn, pp 1111–1116, Oct 1991Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.Dip. Ing. InformazioneUniversità di ParmaParmaItaly

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