Autonomous driving relies on accurate perception to ensure safe driving. Collaborative perception improves accuracy by mitigating the sensing limitations of individual vehicles, such as limited perception range and occlusion-induced blind spots. However, collaborative perception often suffers from high communication overhead due to redundant data transmission, as well as increasing computation latency caused by excessive load with growing connected and autonomous vehicles (CAVs) participation. To address these challenges, we propose a novel local-to-global collaborative perception framework (LGCP) to achieve collaboration in a communication- and computation-efficient manner. The road of interest is partitioned into non-overlapping areas, each of which is assigned a dedicated CAV group to perform localized perception. A designated leader in each group collects and fuses perception data from its members, and uploads the perception result to the roadside unit (RSU), establishing a link between local perception and global awareness. The RSU aggregates perception results from all groups and broadcasts a global view to all CAVs. LGCP employs a centralized scheduling strategy via the RSU, which assigns CAV groups to each area, schedules their transmissions, aggregates area-level local perception results, and propagates the global view to all CAVs. Experimental results demonstrate that the proposed LGCP framework achieves an average 44 times reduction in the amount of data transmission, while maintaining or even improving the overall collaborative performance.

翻译：自动驾驶依赖精确的感知以确保安全驾驶。协同感知通过缓解单车感知的局限性（如感知范围受限和遮挡导致的盲区）来提高准确性。然而，协同感知通常因冗余数据传输而面临高通信开销，并随着网联自动驾驶车辆（CAV）参与数量的增加，因负载过重而导致计算延迟上升。为应对这些挑战，我们提出了一种新颖的局部到全局协同感知框架（LGCP），以通信和计算高效的方式实现协同。将目标道路划分为互不重叠的区域，每个区域分配一个专用的CAV群组执行局部感知。每个群组中指定的领导者收集并融合其成员的感知数据，并将感知结果上传至路侧单元（RSU），从而建立局部感知与全局态势感知之间的连接。RSU聚合所有群组的感知结果，并向所有CAV广播全局视图。LGCP通过RSU采用集中式调度策略，该策略为每个区域分配CAV群组，调度其传输，聚合区域级局部感知结果，并将全局视图传播至所有CAV。实验结果表明，所提出的LGCP框架在保持甚至提升整体协同性能的同时，实现了数据传输量平均降低44倍。