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（Bird's Eye View, BEV）已被用于精确捕捉车辆间的空间关系。然而，BEV存在严重的固有局限性，例如盲区问题。协同感知通过融合多视角周围车辆的数据，成为克服这些局限性的有效解决方案。现有的大多数协同感知策略基于传输公平性采用全连通图结构，却往往因信道变化和感知冗余而忽略单个车辆的重要性差异。针对这些挑战，我们提出了一种新颖的优先级感知协同感知（Priority-Aware Collaborative Perception, PACP）框架，通过BEV匹配机制，基于附近网联自动驾驶车辆与本车之间的相关性确定优先级水平。借助子模优化方法，我们找到了近优的传输速率、链路连接性及压缩指标。此外，我们部署了基于深度学习的自适应自编码器，以在动态信道条件下动态调节图像重建质量。最后，通过大量实验证明，我们的方案在效用指标和交并比精度上分别比现有最优方案提升8.27%和13.60%。