Improving object selection for Collective Perception Messages under congestion

Collective Perception Messages (CPMs), defined by European Telecommunications Standards Institute (ETSI), enable vehicles and roadside infrastructure to exchange information about detected objects, enhancing situational awareness in cooperative environments. However, as the size of CPMs increases — particularly in dense traffic scenarios — the wireless channel can become saturated, leading to delays in transmission and reduced packet delivery ratios. This paper starts by assessing how the number of objects included per CPM impacts communication performance, highlighting the necessity for effective object selection strategies during periods of congestion. To address this issue, we propose a lightweight, real-time object prioritization algorithm based on deviations from the predicted path. Our method estimates each object’s expected state based on its last transmission, and prioritizes those whose current state deviates most from this prediction, as these are likely to be more informative. The evaluation uses a real-world dataset and demonstrates that our strategy significantly improves predictive accuracy by at least 7%. Moreover, the algorithm does not increase CPU or memory usage, demonstrating similar resource consumption compared to the method described in the Collective Perception Service (CPS) standard, making it well-suited for embedded platforms. These results confirm that Prediction–Deviation selection can enhance the efficiency and informativeness of CPMs, especially when the message size must be constrained due to network congestion.