Towards a Simulation Framework for Performance Evaluation of RIS-Assisted Collective Perception Message Exchange

Cooperative, connected and automated mobility (CCAM) is revolutionizing the future of transportation by promising substantial improvements in road safety, traffic efficiency, and driver comfort. Central to CCAM is the concept of collective perception, where vehicles exchange sensor data via vehicle-to-everything (V2X) communication to overcome individual perception limitations. However, communication disruptions, data privacy, and reliability issues persist, particularly when obstacles interfere with the exchange of collective perception messages (CPMs). This paper investigates the potential of reconfigurable intelligent surface (RIS) in addressing these challenges by enhancing V2X communication channels. For the first time, we explore the integration of RIS in a simulation framework designed to model the exchange of CPM messages in cooperative driving environments. Additionally, we perform object detection on a static vehicle’s field of view and transmit the video stream with detection results to a moving vehicle, further enhancing situational awareness. We also present a novel analysis of the trade-off between cooperative perception quality and latency under RIS-assisted communication by evaluating the impact of frame compression on packet loss and information freshness. Using the CoopeRIS platform integrated with CARLA, we demonstrate how RIS-assisted communication can mitigate the impact of obstacles and improve the reliability of CPM exchange, providing a robust foundation for the future of collective perception services in CCAM. Our results show significant gains in communication reliability, detection precision, and the effectiveness of CPM exchange in obstructed environments.