In this contribution we apply the probability hypothesis density (PHD) filter algorithm for joint tracking of an unknown varying number of targets to automotive environment sensing systems. We use data from a vision and a lidar sensor as well as the vehicle ESP system. After deriving a method to parametrise the algorithm systematically from detection performance statistics we proof the applicability of the method for automotive tracking based on real sensor data.