Collective Perception Service with Risk and Redundancy-Based Object Selection in Cellular-V2X

The Collective Perception Service (CPS) enables Connected and Automated Vehicles (CAVs) to distribute data detected by their onboard sensors. Higher vehicle traffic densities, however, can cause communication resource shortages and reduced communication quality. To address this issue, minimizing the traffic volume of Collective Perception Messages (CPMs) becomes necessary. The ETSI standard approach prioritizes objects with high mobility when including them in CPMs. This approach, however, may result in multiple vehicles transmitting data about the same highly dynamic objects simultaneously, which leads to inefficient use of communication bandwidth. Furthermore, only relying on mobility parameters that surpass a threshold does not account for accident risks that depend on the positional and directional relationships between objects and CAVs. This paper proposes a risk and redundancy-based object selection method, RRS, which selects the objects for CPMs based on accident risk and redundancy in C-V2X. We introduced accident avoidance acceleration as a metric to assess accident risk, and redundancy as the number of CPM receptions for an object. Using these two indicators, CAVs calculate the priority of objects for inclusion in CPMs. We evaluate RRS through simulation in a traffic intersection scenario. The results demonstrate that RRS effectively reduces CPM traffic volumes while maintaining object recognition capabilities, even in environments with high CAV densities.