FullPerception: Network-Level Collaborative Perception for Eliminating Vehicular Blind Spots

作者：Lin Liang, Guiyang Luo, Yijing Lin, Lei Deng, Nan Cheng, Quan Yuan, Jinglin Li, Dusit Niyato · 发表于：IEEE Transactions on Mobile Computing · 年份：2025 · DOI：10.1109/tmc.2025.3600060 · 被引用次数：3 · 研究领域：Transportation and Mobility Innovations、Transportation Planning and Optimization、Human Mobility and Location-Based Analysis

Collaborative perception can significantly enhance the perceptual capabilities of autonomous vehicles by sharing sensing information through vehicular communications. However, large-scale sharing of sensing information often results in unsustainable network loads, making it challenging to maximize perception performance with limited communication resources in complex environments. To address this challenge, we propose FullPerception, an innovative cooperative perception framework that jointly orchestrates sensing information sharing and communication resource allocation at the network level. FullPerception advocates for the sharing of semantic information (neural network features) within critical areas, i.e., blind spots. With limited communication resources, FullPerception strategically eliminates these blind spots to maximize the accumulated perception performance. We formulate this strategy as a weighted optimization problem and prove its NP-hardness. We propose a simple yet effective algorithm, Proactive Conflict-free Scheduling (PCS), which guarantees a good performance ratio by considering broader contexts. PCS is meticulously combined with recursive structure, accounting for both the overall and future contexts to determine link scheduling and resource allocation. We demonstrate that FullPerception improves perception accuracy by 20% relative to single-vehicle systems and by 10% compared to existing scheduling methods through large-scale comprehensive joint simulation ...