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
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;
Conference_Titel :
Intelligent Vehicles Symposium, 2004 IEEE
Print_ISBN :
0-7803-8310-9
DOI :
10.1109/IVS.2004.1336491