• DocumentCode
    305481
  • Title

    Collision detection VLSI processor for intelligent vehicles based on efficient coordinate transformation scheme

  • Author

    Hariyama, Masanori ; Kameyama, Michitaka

  • Author_Institution
    Dept. of Comput. & Math. Sci., Tohoku Univ., Sendai, Japan
  • Volume
    2
  • fYear
    1996
  • fDate
    5-10 Aug 1996
  • Firstpage
    755
  • Abstract
    This paper describes a high-performance VLSI processor for the collision detection of intelligent vehicles. In the collision detection, high-computational power is essential in not only coordinate transformation but also matching operation between vehicle and obstacle pixels. In the processor, a content-addressable memory is introduced to store vehicle pixel information, so that the matching operation is drastically accelerated. Since vehicle pixel information is predetermined and not changed, the high-performance CAM (content addressable memory) based on a ROM cell is proposed. A parallel and pipelined architecture for the high-speed coordinate transformation is also proposed based on two-dimensional vector rotations and matrix multiplications
  • Keywords
    VLSI; artificial intelligence; content-addressable storage; parallel architectures; path planning; pipeline processing; position control; ROM cell; collision detection VLSI processor; content addressable memory; efficient coordinate transformation scheme; high-performance CAM; intelligent vehicles; matching operation; matrix multiplications; obstacle pixels; parallel architecture; pipelined architecture; two-dimensional vector rotations; vehicle pixel information; CADCAM; Computer aided manufacturing; Intelligent vehicles; Land vehicles; Read only memory; Road accidents; Shape; Solids; Vehicle detection; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics, Control, and Instrumentation, 1996., Proceedings of the 1996 IEEE IECON 22nd International Conference on
  • Conference_Location
    Taipei
  • Print_ISBN
    0-7803-2775-6
  • Type

    conf

  • DOI
    10.1109/IECON.1996.565972
  • Filename
    565972