Optimizing Cooperative Perception Message Dissemination in Vehicular Networks Through Selective Transmission.

Efficiently disseminating Collective Perception Messages (CPMs) in vehicular networks is pivotal for advancing applications and enhancing road safety. This work addresses the challenge of vehicle selection for CPM transmission, aiming to maximize coverage, minimize congestion, and optimize resource utilization. We introduce a novel greedy approximation-based vehicle selection method that strategically chooses vehicles considering all nearby road objects, with the goal of covering a majority of them while conserving resources. The algorithm selects vehicles to transmit CPMs based on their proximity to objects in the network and other vehicles. Extensive simulations showcase the effectiveness of our algorithm in achieving efficient CPM dissemination, resulting in a significant reduction in network load.