Infrastructure-supported Perception and Track-level Fusion using Edge Computing

Abstract

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.