Object tracking based on evidential dynamic occupancy grids in urban environments

Occupancy grid mapping approaches, especially those that additionally estimate the dynamics, enable a robust and consistent modeling of the local environment in a cell-level representation. But a scene understanding of surrounding traffic participants requires a generalized object-level representation. This work presents an object tracking approach based on dynamic occupancy grids. The association of occupied grid cells with existing object tracks is solved individually on the cell-level without clustering or forming object hypotheses. New object tracks are extracted using a clustering strategy and a velocity variance analysis of neighboring occupied cells to reduce false positives. In order to improve the estimates of the position and size, an object boundary extraction is presented that takes the surrounding free space of the selected box representation into account. Experimental results with real sensor data show the effectiveness of the proposed object tracking approach in challenging urban scenarios with dense traffic.