Data from infrastructure sensors can significantly improve the field of view for intelligent vehicles (IV), both in terms of range and completeness. In the MEC-View project, we investigate how automated driving (AD) can benefit from incorporating such data in the perception processing chain. On the infrastructure side, a central computational node, called MEC-Server, is connected to a base station and receives objects from multiple roadside sensors. Those are used to create a fused environmental model, which is distributed to vehicles close by via a managed cellular network. To use tracks received from the MEC-Server in IV perception, we propose a hybrid vehicular perception system that is able to fuse both local onboard sensor data as well as estimations by the MEC-Server. For this, we discuss multiple approaches to track-level fusion and data association, including their application in our perception system. Using careful interface design, we are able to avoid many non-linearities and are able to minimize the amount of approximations involved. For evaluation, we present a experimental setup for track-level fusion schemes that is based on virtually augmented real-world measurements and facilitates targeted adaptation of influencing variables while ensuring real-world applicability. A comparison of different fusion schemes provides insights into their relative performance and shows directions for real-world applicability.