Uli Kolbe

Multi-target multi-object tracking, sensor fusion of radar and infrared

This paper presents algorithms and techniques for single-sensor tracking and multi-sensor fusion of infrared and radar data. The results show that fusing radar data with infrared data considerably increases detection range, reliability and accuracy of the object tracking. This is mandatory for further development of driver assistance systems. Using multiple model filtering for sensor fusion applications helps to capture the dynamics of maneuvering objects while still achieving smooth object tracking for not maneuvering objects. This is important when safety and comfort systems have to make use of the same sensor information. Comfort systems generally require smoothly filtered data whereas for safety systems it is crucial to capture maneuvers of other road users as fast as possible. Multiple model filtering and probabilistic data association techniques are presented and all presented algorithms are tested in real-time on standard PC systems.

Multi-target multi-object radar tracking

In this paper, we present algorithms to exploit full capabilities of an automotive adaptive cruise control radar sensor. We propose model based filtering and data association techniques, guaranteeing sufficient noise suppression without constraining filter dynamics more than necessary. All measured data are considered and the tracking is extended to the multi-object case.