Infrastructure-enhanced Multi-target Tracking Using a Multiple-model PHD Filter

Abstract

Environment perception is crucial for the development of autonomous driving and advanced driver assistance systems. The cooperative perception using the infrastructure sensors can significantly expand the field of view of on-board sensors and improve the accuracy of target tracking. In this paper, we propose a hybrid vehicular perception system that incorporates both received feature-level information from infrastructure sensors and track-level data from the multi-access edge computing server (MEC-Server). An infrastructure-enhanced multiple-model probability hypothesis density is proposed to handle the feature-level data from heterogeneous infrastructure sensors. The problem of kinematic state estimation is improved with the prior information of the road environment. Furthermore, a generic communication interface between the infrastructure sensor and MEC-Server is designed, which allows the object data to have the same notion of locality through the use of a generic object state model. Simulation results show that the presented algorithm provides higher accuracy and reliability after considering the prior information of the road environment.