Intersection-Based Road User Tracking Using a Classifying Multiple-Model PHD Filter

Abstract

The number of fatal accidents involving pedestrians and bikers at urban intersections is still increasing. Therefore, an intersection-based perception system provides a dynamic model of the intersection scene to the vehicles. Based on that, the intersection perception facilitates to discriminate occlusions which is expected to significantly reduce the number of accidents at intersections. Therefore this contribution presents a general purpose multi-sensor tracking algorithm, the classifying multiple-model probability hypothesis density (CMMPHD) filter, which facilitates the tracking and classification of relevant objects using a single filter. Due to the different motion characteristics, a multiple-model approach is required to obtain accurate state estimates and persistent tracks for all types of objects. Additionally, an extension of the PHD filter to handle contradictory measurements of different sensor types based on the Dempster-Shafer theory of evidence is proposed. The performance of tracking and classification is evaluated using real world sensor data of a public intersection.