• DocumentCode
    2839794
  • Title

    Adaptive road detection through continuous environment learning

  • Author

    Foedisch, Mike ; Takeuchi, Aya

  • Author_Institution
    Div. of Intelligent Syst., Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
  • fYear
    2004
  • fDate
    13-15 Oct. 2004
  • Firstpage
    16
  • Lastpage
    21
  • Abstract
    The Intelligent Systems Division of the National Institute of Standards and Technology has been engaged for several years in developing real-time systems for autonomous driving. A road detection program is an essential part of the project. Previously we developed an adaptive road detection system based on color histograms using a neural network. This, however, still required human involvement during the initialization step. As a continuation of the project, we have expanded the system so that it can adapt to the new environment without any human intervention. This system updates the neural network continuously based on the road image structure. In order to reduce the possibility of misclassifying road and non-road, we have implemented an adaptive road feature acquisition method.
  • Keywords
    learning (artificial intelligence); neural nets; object detection; road traffic; adaptive road detection; adaptive road feature acquisition; autonomous driving; color histograms; continuous environment learning; neural network; Adaptive systems; Feature extraction; Filters; Histograms; Humans; Intelligent systems; NIST; Neural networks; Real time systems; Roads;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory, 2004. ISIT 2004. Proceedings. International Symposium on
  • ISSN
    1550-5219
  • Print_ISBN
    0-7695-2250-5
  • Type

    conf

  • DOI
    10.1109/AIPR.2004.9
  • Filename
    1409668