Simulation-based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering

V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles or roadside units, by exchanging lists of perceived objects in the 5.9GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety, e.g., vehicles or pedestrians. The current standardization of Collective Perception in Europe considers filtering objects for transmission based on their locally perceived dynamics and freshness to preserve channel resources. However, two remaining problems of object filtering are: information redundancy and adapting object filtering to the available channel resources. In this paper, we combine redundancy mitigation and congestion control-aware filtering. We evaluate the performance of the resulting object filtering techniques by realizing realistic, large-scale simulations of a mid-size city in Germany. We assess the performance using a scoring metric. The results show better information redundancy control and adjustable channel usage for object filtering.