Machine Vision and Applications

, Volume 22, Issue 5, pp 819–835 | Cite as

Automatic LightBeam Controller for driver assistance

  • P. F. Alcantarilla
  • L. M. BergasaEmail author
  • P. Jiménez
  • I. Parra
  • D. F. Llorca
  • M. A. Sotelo
  • S. S. Mayoral
Original Paper


In this article, we present an effective system for detecting vehicles in front of a camera-assisted vehicle (preceding vehicles traveling in the same direction and oncoming vehicles traveling in the opposite direction) during night-time driving conditions in order to automatically change vehicle head lights between low beams and high beams avoiding glares for the drivers. Accordingly, high beams output will be selected when no other traffic is present and will turn low beams on when other vehicles are detected. In addition, low beams output will be selected when the vehicle is in a well lit or urban area. LightBeam Controller is used to assist drivers in controlling vehicle’s beams increasing its correct use, since normally drivers do not switch between high beams and low beams or vice versa when needed. Our system uses a B&W forward looking micro-camera mounted in the windshield area of a C4-Picasso prototype car. Image processing techniques are applied to analyse light sources and to detect vehicles in the images. Furthermore, the system is able to classify between vehicle lights and road signs reflections or nuisance artifacts by means of support vector machines. The algorithm is efficient and able to run in real time. The system has been tested with different video sequences (more than 7 h of video sequences) under real night driving conditions in different roads of Spain. Experimental results, a comparison with other representative state of the art methods and conclusions about the system performance are presented.


Computer vision Driver-assistance systems Head-lights detection Tail-lights detection Support vector machines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    U.S. Department of Transportation: Regulations of the light emissions of vehicle high beam headlamps (2007).
  2. 2.
    Akashi, Y., Rea, M.: The effect of oncoming headlight glare on peripheral detection under a mesopic light level. In: Progress in Automotive Lighting (2001)Google Scholar
  3. 3.
    GENTEX: Vehicle lamp control (2005).
  4. 4.
    Mobileye: Adaptative headlight control (2007).
  5. 5.
    Chen Y.L. (2009) Nighttime vehicle light detection on a moving vehicle using image segmentation and analysis techniques. WSEAS Trans. Comput. 8(3): 506–515Google Scholar
  6. 6.
    O’Malley, R., Glavin, M., Jones, E.: Vehicle detection at night based on tail-light detection. In: 1st International Symposium on Vehicular Computing Systems, Trinity College Dublin (2008)Google Scholar
  7. 7.
    López, A., Hilgenstock, J., Busse, A., Baldrich, R., Lumbreras, F., Serrat, J.: Nighttime vehicle detection for intelligent headlight control. In: ACIVS 08: Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 113–124. Springer, Berlin (2008)Google Scholar
  8. 8.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the European Conference on Computational Learning Theory, pp. 23–37 (1995)Google Scholar
  9. 9.
    Alcantarilla, P.F., Bergasa, L.M., Jiménez, P., Sotelo, M.A., Parra, I., Fernández, D., Mayoral, S.S. : Night time vehicle detection for driving assistance. In: IEEE Intelligent Vehicles Symposium (IV) (2008)Google Scholar
  10. 10.
    Clanton J.M., Bevly D.M., Hodel A.S. (2009) A low-cost solution for an integrated multisensor lane departure warning system. IEEE Trans. Intell. Transp. Syst. 10: 47–59CrossRefGoogle Scholar
  11. 11.
    Parra I., Fernández D., Sotelo M.A., Bergasa L.M., Revenga P., Nuevo J., Ocaña M., García M.A. (2007) A combination of feature extraction methods for SVM pedestrian detection. IEEE Trans. Intell. Transp. Syst. 8(2): 292–307CrossRefGoogle Scholar
  12. 12.
    Otsu N. (1979) A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9: 62–66CrossRefGoogle Scholar
  13. 13.
    Welch, G., Bishop, G.: An introduction to the Kalman filter. Tech. Rep., University of North Carolina at Chapel Hill, Department of Computer Science (2001)Google Scholar
  14. 14.
    Bertozzi, M., Broggi, A., Fascioli, A., Tibaldi, A.: Pedestrian localization and tracking system with Kalman filtering. In: IEEE Intelligent Vehicles Symposium (IV), pp. 584–589 (2004)Google Scholar
  15. 15.
    Dellaert, F., Thorpe, C.: Robust car tracking using Kalman filtering and Bayesian templates. In: Conference on Intelligent Transportation Systems (1997)Google Scholar
  16. 16.
    Jain A. (1986) Fundamentals of Digital Image Processing. Prentice-Hall, Englewood CliffsGoogle Scholar
  17. 17.
    Derong, Y., Yuanyuan, Z., Dongguo, L.: Fast computation of multiscale morphological operations for local contrast enhancement. In: IEEE Medicine and Biology 27th Annual Conference (2005)Google Scholar
  18. 18.
    Lindeberg T. (1998) Feature detection with automatic scale selection. Int. J. Comput. Vis. 30(2): 77–116Google Scholar
  19. 19.
    Dickmanns E.D., Mysliwetz B.D. (1992) Recursive 3-D road and relative ego-state recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(2): 199–213CrossRefGoogle Scholar
  20. 20.
    Alcantarilla, P., Sotelo, M.A., Bergasa, L.M.: Automatic daytime road traffic control and monitoring system. In: IEEE Intelligent Transportations Systems Conference (ITSC), pp. 944–949 (2008)Google Scholar
  21. 21.
    Hu M. (1962) Visual pattern recognition by moments. IRE Trans. Inf. Theory 8(2): 179–187CrossRefGoogle Scholar
  22. 22.
    Hupkens T.M., de Clippeleir J. (1995) Noise and intensity invariant moments. Pattern Recognit. 16: 371–376CrossRefGoogle Scholar
  23. 23.
    Enzweiler M., Gavrila D.M. (2009) Monocular pedestrian detection: survey and experiments. IEEE. Trans. Pattern Anal. Mach. Intell. 31(12): 2179–2195CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • P. F. Alcantarilla
    • 1
  • L. M. Bergasa
    • 1
    Email author
  • P. Jiménez
    • 1
  • I. Parra
    • 1
  • D. F. Llorca
    • 2
  • M. A. Sotelo
    • 2
  • S. S. Mayoral
    • 3
  1. 1.Department of ElectronicsUniversity of AlcaláMadridSpain
  2. 2.Department of AutomationUniversity of AlcaláMadridSpain
  3. 3.FICO MIRRORS, SA, Research DepartmentBarcelonaSpain

Personalised recommendations