• DocumentCode
    3467747
  • Title

    IMM object tracking for high dynamic driving maneuvers

  • Author

    Kaempchen, Nico ; Weiss, Kristian ; Schaefer, Michael ; Dietmayer, Klaus C J

  • Author_Institution
    Dept. of Meas., Control & Microtechnol., Ulm Univ., Germany
  • fYear
    2004
  • fDate
    14-17 June 2004
  • Firstpage
    825
  • Lastpage
    830
  • Abstract
    Classical object tracking approaches use a Kalman-filter with a single dynamic model which is therefore optimised to a single driving maneuver. In contrast the interacting multiple model (IMM) filter allows for several parallel models which are combined to a weighted estimate. Choosing models for different driving modes, such as constant speed, acceleration and strong acceleration changes, the object state estimation can be optimised for highly dynamic driving maneuvers. The paper describes the analysis of Stop&Go situations and the systematic parametrisation of the IMM method based on these statistics. The evaluation of the IMM approach is presented based on real sensor measurements of laser scanners, a radar and a video image processing unit.
  • Keywords
    Kalman filters; filtering theory; object detection; optimisation; probability; traffic engineering computing; Stop-Go situations; dynamic Kalman filter modelling; high dynamic driving maneuvers; interacting multiple model filter; laser scanners; object state estimation; object tracking; optimisation; parallel modelling; probability; sensor measurements; systematic parametrisation; traffic jam situations; video image processing unit; Acceleration; Filters; Image processing; Image sensors; Laser radar; Radar imaging; Radar measurements; Radar tracking; State estimation; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Vehicles Symposium, 2004 IEEE
  • Print_ISBN
    0-7803-8310-9
  • Type

    conf

  • DOI
    10.1109/IVS.2004.1336491
  • Filename
    1336491