Rainer Dipl.-Ing. Möbus

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-object adaptive cruise control

In this paper we propose an algorithm for solving a Multi-Object Adaptive Cruise Control problem.In a multi-object traffic scene the optimal acceleration is to be found respecting traffic rules, safety distances and driver intentions.The objective function is modelled as a quadratic cost function for the discrete time piecewise affine system. We find the optimal state-feedback control law by solving the underlying constrained finite time optimal control problem via dynamic programming.