IEEE Transactions on Mobile Computing最新文献

英文中文

PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehicles PACP：面向互联和自动驾驶汽车的优先感知协同感知技术

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Mobile Computing

Pub Date : 2024-08-26 DOI: 10.1109/TMC.2024.3449371

Zhengru Fang;Senkang Hu;Haonan An;Yuang Zhang;Jingjing Wang;Hangcheng Cao;Xianhao Chen;Yuguang Fang

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.

对于联网和自动驾驶汽车（CAVs）来说，周围环境感知是安全驾驶的关键。然而，BEV 存在严重的固有局限性，如盲点。通过对周围车辆的多个视图进行数据融合，协同感知已成为克服这些局限性的有效解决方案。虽然现有的协同感知策略大多采用以传输公平性为前提的全连接图，但由于信道变化和感知冗余，它们往往忽略了单个车辆的不同重要性。为了应对这些挑战，我们提出了一种新颖的优先级感知协同感知（PACP）框架，采用 BEV 匹配机制，根据附近 CAV 与自我感知车辆之间的相关性确定优先级。通过利用次模态优化，我们找到了接近最优的传输速率、链路连接和压缩指标。此外，我们还部署了基于深度学习的自适应自动编码器，以调节动态信道条件下的图像重建质量。最后，我们进行了广泛的研究，并证明我们的方案在 "交集 "与 "联合 "的效用和精度方面分别比最先进的方案高出 8.27% 和 13.60%。

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