• DocumentCode
    3467403
  • Title

    Making Bertha See

  • Author

    Franke, Ulrik ; Pfeiffer, David ; Rabe, Clemens ; Knoeppel, Carsten ; Enzweiler, Markus ; Stein, Fridtjof ; Herrtwich, Ralf G.

  • Author_Institution
    R&D, Daimler AG, Sindelfingen, Germany
  • fYear
    2013
  • fDate
    2-8 Dec. 2013
  • Firstpage
    214
  • Lastpage
    221
  • Abstract
    With the market introduction of the 2014 Mercedes-Benz S-Class vehicle equipped with a stereo camera system, autonomous driving has become a reality, at least in low speed highway scenarios. This raises hope for a fast evolution of autonomous driving that also extends to rural and urban traffic situations. In August 2013, an S-Class vehicle with close-to-production sensors drove completely autonomously for about 100 km from Mannheim to Pforzheim, Germany, following the well-known historic Bertha Benz Memorial Route. Next-generation stereo vision was the main sensing component and as such formed the basis for the indispensable comprehensive understanding of complex traffic situations, which are typical for narrow European villages. This successful experiment has proved both the maturity and the significance of machine vision for autonomous driving. This paper presents details of the employed vision algorithms for object recognition and tracking, free-space analysis, traffic light recognition, lane recognition, as well as self-localization.
  • Keywords
    image sensors; mobile robots; object recognition; object tracking; stereo image processing; traffic engineering computing; 2014 Mercedes-Benz S-Class vehicle; Bertha Benz memorial route; Germany; Mannheim; Pforzheim; autonomous driving; close-to-production sensors; complex traffic situations; free-space analysis; lane recognition; low speed highway scenarios; market introduction; narrow European villages; next-generation stereo vision; object recognition; object tracking; rural traffic situations; self-localization; sensing component; stereo camera system; traffic light recognition; urban traffic situations; vision algorithms; Cameras; Global Positioning System; Machine vision; Roads; Sensors; Stereo vision; Vehicles; Autonomous Driving; Intelligent Vehicles; Stereo Vision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on
  • Conference_Location
    Sydney, NSW
  • Type

    conf

  • DOI
    10.1109/ICCVW.2013.36
  • Filename
    6755901