RSOF: Receiver-Side Object Filtering for Scalable Collective Perception Object Fusion

Collective Perception (CP) is a key use case for the Cooperative-Intelligent Transportation System (C-ITS) to improve traffic safety. With CP, vehicles and infrastructure share information about locally detected objects via direct broadcast communication to enhance each other's awareness and obtain perception beyond line-of-sight. However, when more and more vehicles deploy CP, the resource demand grows in terms of communication as more and more senders share their object observations. Moreover, computation requirements grow throughout a vehicle's lifetime as more observations must be processed and fused into a holistic Environment Model. This paper proposes Receiver-Side Object Filtering (RSOF), which filters received objects by relevance and quality, reducing computing power requirements and considering constant memory. The evaluation in a large-scale traffic scenario shows that RSOF significantly reduces the number of fusion operations required to process an increasing amount of received object data. Furthermore, an upper bound for memory and fusion operations is achieved.