• DocumentCode
    3363123
  • Title

    Structural Road Detection for Intelligent Vehicle Based on a 2d Laser Radar

  • Author

    Feng, Mingyue ; Jia, Peng ; Wang, Xiao ; Liu, Hongquan ; Cao, Jian

  • Author_Institution
    Dept. of Autom. Eng., Mil. Transp. Univ., Tianjin, China
  • Volume
    1
  • fYear
    2012
  • fDate
    26-27 Aug. 2012
  • Firstpage
    293
  • Lastpage
    296
  • Abstract
    Road detection is a key task in developing an intelligent vehicle. Typical methods using machine vision suffer from being easy to be influences by outer conditions, so here we designed a new method to detect roads by a laser radar. Considering the intelligent vehicle may face much trouble in unsmoothed road, in road with anomalous edges or running crosswise with the road direction, we present a method named rectangle-searching algorithm to deal with these problems. In this algorithm, a rectangle is found out by the basis that the number of points involved in the rectangle should be as many as possible, while the size of the rectangle should keep small enough. After finding out the rectangle, an algorithm based on standard deviation is introduced to extract road edge points from road lines, which may help getting the road line from the rectangle. Experimental results validate efficiency and effectiveness of the algorithm.
  • Keywords
    automated highways; computer vision; optical radar; road vehicle radar; 2D laser radar; intelligent vehicle; machine vision; rectangle-searching algorithm; structural road detection; unsmoothed road; Algorithm design and analysis; Image edge detection; Intelligent vehicles; Laser radar; Radar detection; Roads; laser radar; rectangle-searching algorithm; structural road detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
  • Conference_Location
    Nanchang, Jiangxi
  • Print_ISBN
    978-1-4673-1902-7
  • Type

    conf

  • DOI
    10.1109/IHMSC.2012.80
  • Filename
    6305684