BEVCooper：车联网中准确且通信高效的鸟瞰图感知
BEVCooper: Accurate and Communication-Efficient Bird's-Eye-View Perception in Vehicular Networks
AI总结：
BEVCooper通过协作感知框架提升车联网中鸟瞰图感知的准确性和通信效率，减少延迟并优化特征融合。
摘要：
鸟瞰图（BEV）对连接和自动化车辆（CAVs）至关重要，因为它可以提供车辆周围环境的统一且精确的表示。然而，原始传感数据的质量可能会在被遮挡或远处的区域退化，从而影响构建的BEV地图的保真度。在本文中，我们提出了BEVCooper，一种新颖的协作感知框架，能够保证准确且低延迟的BEV地图构建。我们首先定义了一个有效的度量标准来评估来自邻近CAVs的BEV特征的效用。然后，基于此，我们开发了一种基于在线学习的协作CAV选择策略，该策略捕捉了邻近车辆的BEV特征效用的不断变化，使车辆能够优先选择最有价值的来源，以在车辆到车辆（V2V）链路带宽受限的情况下。此外，我们设计了一种自适应融合机制，根据环境动态和实时V2V信道质量优化BEV特征压缩，有效平衡了特征传输延迟和构建的BEV地图的准确性。理论分析表明，BEVCooper在动态车辆拓扑和V2V信道条件下实现了渐近最优的CAV选择和自适应特征融合。广泛的实验表明，在现实世界测试床上，与最先进的基准相比，所提出的BEVCooper将BEV感知精度提高了高达63.18%，并将端到端延迟减少了67.9%，仅增加了1.8%的额外计算开销。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
