A contour classifying Kalman filter based on evidence theory

In the project Stadtpilot, introduced in [1], the object based environment perception system developed by the urban challenge team CarOLO at Technische Universita¨t Braunschweig, as presented in [2], has been enhanced. The context of this new project is more challenging as now because it includes public traffic on large inner-city loops. Other vehicles are described by the project's sensor data fusion by an open polyline (contour) with many points. Some of these points lie on straight lines or they represent noise of the contour which do not contribute to the object's description. These extra points complicate an effective tracking and deform the contour of the object hypothesis. Because of the numerous traffic and due to the change in the environment's type, surrounded vehicles very often create a change of view. This results in no or less measurement updates of some points in the contour and can result in its deformation. In an effort to overcome this problem, the contour estimating Kalman filter, presented in [3], has been enhanced by improved point update algorithms as well as a contour classifier based upon evidence theory. These enhancements allow the decrease of the used points. Changes of view, due to passing traffic, are better identified because the classifier identifies the most likely shape explicitly.