Poster: Risk and Redundancy-Based Object Selection Method for Collective Perception Messages in Cellular-V2x

Connected and Automated Vehicles (CAVs) utilize Vehicle-to-Everything (V2X) communication to exchange messages and share information with surrounding vehicles. Collective Perception Messages (CPMs) contain object information detected by the onboard sensors. Through the exchange with nearby CAVs, CAVs can perceive the surrounding objects. Higher vehicle traffic densities, however, can lead to a shortage of communication resources and degradation of communication quality. This, as a result, leads to a decreased object recognition frequency. To mitigate such degradation, it is necessary to reduce the traffic volume of CPMs. Therefore, it is important to decrease the message size of CPMs. Various existing studies aimed at it have reduced the redundancy of CPMs. Most of the studies, however, do not prioritize the inclusion of high-accident risk objects in CPMs, which leads to a higher Age of Information (AoI) for such objects. In this paper, we propose a risk and redundancy-based object selection method, called RRS, which selects objects for CPMs based on the accident risks for surrounding CAVs and the redundancy of CPMs in C-V2X. In this proposal, we define an accident avoidance acceleration a as an indicator of the accident risks. Based on a for surrounding CAVs and the number of CPM receptions indicating the redundancy, CAV s calculate the priority of objects for CPMs. CAV s select a fixed number of objects based on the priority for CPMs. We evaluate RRS through simulation experiments in an intersection scenario. The results show that RRS decreases the AoI for objects with high accident risks and the number of the highest risks.