Object tracking in urban intersections based on active use of a priori knowledge: Active interacting multi model filter

The perception of moving objects in urban environments poses new challenges for sensors and tracking algorithms. Tracking algorithms must estimate the movement of a great number of objects simultaneously while taking into account longitudinal and crossing, as well as turning traffic. In this process, the early recognition and stable tracking of a turning maneuver is a particular challenge. We present an efficient tracking algorithm that faces this task based on laser scanner data. It uses three different dynamic models for each object simultaneously: One for an object driving straight ahead, one for turning left and one for turning right. We use a priori knowledge to recognize the objects´ maneuvers early and to decide which of the three dynamic models is currently appropriate. Based on the objects´ current positions in the intersection, we determine possible turning maneuvers and use them actively in the filtering process. This method enables an early recognition of the object movement and a stable tracking of longitudinal, crossing, and turning traffic.