• DocumentCode
    3339375
  • Title

    Automated safety inspection of grade crossings

  • Author

    Ranganathan, Pradeep ; Olson, Edwin

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univeristy of Michigan, Ann Arbor, MI, USA
  • fYear
    2010
  • fDate
    18-22 Oct. 2010
  • Firstpage
    2149
  • Lastpage
    2154
  • Abstract
    A grade crossing is a crossing of a railway line and a motor road. In 2009 alone there were 248 deaths and 682 injuries at grade crossings in the United States. Factors like the elevation profile of a crossing or the environment and foliage around the crossing can render it unsafe. Often, vehicles with low ground clearance bottom out on a crossing with a humped elevation profile. Excessive foliage around the crossing can obstruct the visibility of an approaching train, reducing the time a driver has to stop. Hence ensuring safety requires regular monitoring and timely maintenance of grade crossings across the country. In this paper, we describe our method for automatically inspecting grade crossings. Our work employs principled machine learning methods to detect grade crossings from sensor data and then reconstructs the profile of that rail-road intersection. We then show how traffic simulation on the reconstructed profile can be used to determine whether the crossing is unsafe.
  • Keywords
    learning (artificial intelligence); railway electrification; railway engineering; railway safety; traffic control; automated safety inspection; automatically inspecting grade crossing; humped elevation profile; low ground clearance bottom; motor road; principled machine learning method; rail road intersection; railway line; timely maintenance; traffic simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
  • Conference_Location
    Taipei
  • ISSN
    2153-0858
  • Print_ISBN
    978-1-4244-6674-0
  • Type

    conf

  • DOI
    10.1109/IROS.2010.5651812
  • Filename
    5651812