BEVCooper: Accurate and Communication-Efficient Bird's-Eye-View Perception in Vehicular Networks

Bird's-Eye-View (BEV) is critical to connected and automated vehicles (CAVs) as it can provide unified and precise representation of vehicular surroundings. However, quality of the raw sensing data may degrade in occluded or distant regions, undermining the fidelity of constructed BEV map. In this paper, we propose BEVCooper, a novel collaborative perception framework that can guarantee accurate and low-latency BEV map construction. We first define an effective metric to evaluate the utility of BEV features from neighboring CAVs. Then, based on this, we develop an online learning-based collaborative CAV selection strategy that captures the ever-changing BEV feature utility of neighboring vehicles, enabling the ego CAV to prioritize the most valuable sources under bandwidth-constrained vehicleto-vehicle (V2V) links. Furthermore, we design an adaptive fusion mechanism that optimizes BEV feature compression based on the environment dynamics and real-time V2V channel quality, effectively balancing feature transmission latency and accuracy of the constructed BEV map. Theoretical analysis demonstrates that, BEVCooper achieves asymptotically optimal CAV selection and adaptive feature fusion under dynamic vehicular topology and V2V channel conditions. Extensive experiments on real-world testbed show that, compared with state-of-the-art benchmarks, the proposed BEVCooper enhances BEV perception accuracy by up to 63.18% and reduces end-to-end latency by 67.9%, with only 1.8% additional computational overhead.

1 How can the ego CAV select an optimal set of collaborative CAVs to construct an accurate BEV map? Owing to vehicular mobility, collaborative CAVs provide continuouslyevolving and varying levels of contribution to the ego CAV's BEV map construction. Under a limited selection budget, the ego CAV must identify the most valuable collaborators by evaluating their BEV feature utility in real time.

2 How can the ego CAV ensure timely BEV map construction in the presence of the straggler effect induced by heterogeneous V2V link quality? In collaborative BEV perception, the ego CAV cannot initiate data fusion and BEV