Vehicle Tracking Using Extended Object Methods: An Approach for Fusing Radar and Laser

Abstract

Combining data from heterogeneous sensors allows to enhance tracking systems by increasing the field of view, incorporating redundancy, and improving the performance by exploiting complementary sensor characteristics. This paper proposes a new vehicle tracking approach for vehicle environment perception that fuses radar and laser data. A Random-Finite-Set-based tracking filter, which permits a clear mathematical formulation of the multi-object problem, is used as fusion center. In combination with extended object measurement models that work on the raw sensor data directly, the filter uses all available information without the need for further preprocessing routines, considers object interdependencies, and works in ambiguous situations. The results are evaluated using experimental data from a test vehicle.