Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial relationships among vehicles. However, severe inherent limitations of BEV, like blind spots, have been identified. Collaborative perception has emerged as an effective solution to overcoming these limitations through data fusion from multiple views of surrounding vehicles. While most existing collaborative perception strategies adopt a fully connected graph predicated on fairness in transmissions, they often neglect the varying importance of individual vehicles due to channel variations and perception redundancy. To address these challenges, we propose a novel Priority-Aware Collaborative Perception (PACP) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception. By leveraging submodular optimization, we find near-optimal transmission rates, link connectivity, and compression metrics. Moreover, we deploy a deep learning-based adaptive autoencoder to modulate the image reconstruction quality under dynamic channel conditions. Finally, we conduct extensive studies and demonstrate that our scheme significantly outperforms the state-of-the-art schemes by 8.27\% and 13.60\%, respectively, in terms of utility and precision of the Intersection over Union.

翻译：周围环境感知对于网联自动驾驶车辆的安全行驶至关重要，其中鸟瞰图已被用于精确捕捉车辆间的空间关系。然而，鸟瞰图存在严重的固有局限性，例如盲区。协同感知通过融合来自周围车辆多视角的数据，已成为克服这些局限性的有效解决方案。虽然现有的大多数协同感知策略采用基于传输公平性的全连接图，但它们往往忽略了由于信道变化和感知冗余导致的单个车辆的重要性差异。为应对这些挑战，我们提出了一种新颖的优先级感知协同感知框架，该框架采用BEV匹配机制，根据附近CAV与自车之间的感知相关性来确定优先级。通过利用次模优化，我们找到了接近最优的传输速率、链路连接性和压缩度量。此外，我们部署了一个基于深度学习的自适应自编码器，以在动态信道条件下调节图像重建质量。最后，我们进行了广泛的研究，结果表明我们的方案在效用和交并比精度方面分别显著优于现有最优方案8.27%和13.60%。