Network Load Adaptation for Collective Perception in V2X Communications

Abstract

Collective perception uses V2X communications to increase the perception capabilities of vehicles. Relying on the perceived data from their local sensors, nodes exchange information about the objects they detect in their surroundings. An object can be anything significant for the nodes' safety, e.g., obstacles on the road, other vehicles or pedestrians. The amount of data generated by each node is determined by the number of perceived objects and the generation frequency of the messages carrying the detected objects. Considering the limited bandwidth of the wireless channel, the data load generated by collective perception can easily exceed the channel capacity. In this paper, we investigate three schemes that filter the number of objects in the messages and thereby adjust the network load in order to optimize the transmission of perceived objects. Our simulation-based performance evaluation indicates that the use of filtering is an effective approach to improve network-related performance metrics, whereas the expected impairment of the perception quality is rather small. The comparison of the filtering algorithms provide insights into the tradeoff between network-related metrics and perception quality.