The use of Cooperative Perception (CP) enables Connected and Autonomous Vehicles (CAVs) to exchange objects perceived from onboard sensors (e.g., radars, lidars, and cameras) with other CAVs via CP messages (CPMs) through Vehicle-to-Vehicle (V2V) communication technologies. However, the same objects in the driving environment may simultaneously appear in the line of sight of multiple CAVs. Consequently, this leads to much irrelevant and redundant information being exchanged in the V2V network. This overloads the communication channel and reduces the CPM delivery to CAVs, thereby decreasing CP awareness. To address this issue, we mathematically formulate CP information usefulness as a maximization problem in a multi-CAV environment and introduce a distributed multi-agent deep reinforcement learning approach based on the double deep Qlearning algorithm to solve it. This approach allows each CAV to learn an optimal CPM content selection policy that maximizes the usefulness of surrounding CAVs as much as possible to reduce redundancy in the V2V network. Simulation results highlight that the proposal effectively mitigates object redundancy and improves network reliability, ensuring increased awareness at short and medium distances of less than 200 m compared to state-of-the-art approaches.

Cooperative Perception (CP) allows Connected and Autonomous Vehicles (CAVs) to enhance their Environmental Awareness (EA) by sharing locally perceived objects through CP messages (CPMs). The European Telecommunications Standards Institute has recently defined a set of CPM generation rules to achieve a trade-off between EA and Channel Busy Ratio (CBR) despite massive perception data. Nonetheless, these rules still lack the consideration of information usefulness, resulting in a considerable volume of useless information transmitted in the CP network. This limitation could increase CBR and thus decrease EA due to the loss of CPMs in the network. This paper introduces CloudAC-IU, a cloud-based deep reinforcement learning approach to learn CAVs to maximize perception information usefulness in the network. Simulation results highlight that CloudAC-IU enhances EA by decreasing CBR and increasing CPM reception for CAVs compared to the state-ofthe-art works.