Multiple extended objects tracking with object-local occupancy grid maps

Improved sensors in the automotive field are leading to multi-object tracking of extended objects becoming more and more important for advanced driver assistance systems and highly automated driving. This paper proposes an approach that combines a PHD filter for extended objects, viz. objects that originate multiple measurements while also estimating the shape of the objects via constructing an object-local occupancy grid map and then extracting a polygonal chain. This allows tracking even in traffic scenarios where unambiguous segmentation of measurements is difficult or impossible. In this work, this is achieved using multiple segmentation assumptions by applying different parameter sets for the DBSCAN clustering algorithm. The proposed algorithm is evaluated using simulated data and real sensor data from a test track including highly accurate D-GPS and IMU data as a ground truth.