Road user tracking at intersections using a multiple-model PHD filter

Abstract

A major aim of the joint project Ko-PER is the mitigation of fatal accidents at urban intersections. Therefore several test intersections have been equipped with multiple laser range finders to recognize and track road users. Besides a high traffic density the variety of road users is challenging. In this contribution a multiple-model (MM) probability hypothesis density filter with a track representation extended by class probabilities is proposed. The approach enables tracking of road users with appropriate motion models using a single MM filter. Due to the estimation of the class probabilities an adaption of the transition probabilities between the models is possible. The performance of the road user tracking is evaluated using real world data.