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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. <\/jats:p>","DOI":"10.1142\/s0218126621501231","type":"journal-article","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T08:09:21Z","timestamp":1603872561000},"page":"2150123","source":"Crossref","is-referenced-by-count":2,"title":["Infrastructure-enhanced Multi-target Tracking Using a Multiple-model PHD Filter"],"prefix":"10.1142","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-9388","authenticated-orcid":false,"given":"Zhen","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, No.2 Chongwen Road, Nan\u2019an District, Chongqing 400065, P. R. 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