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

TL;DR

BEVCooper is a collaborative perception framework for connected vehicles that improves BEV map accuracy and reduces latency through adaptive feature fusion and vehicle selection under V2V constraints.

Contribution

It introduces an online learning-based CAV selection strategy and an adaptive fusion mechanism for efficient, accurate BEV perception in vehicular networks.

Findings

BEVCooper improves BEV perception accuracy by up to 63.18%.

It reduces end-to-end latency by 67.9%.

It maintains low computational overhead of 1.8%.

Abstract

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 vehicle-to-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.