Collaborative Perception (CP) has been a promising solution to address occlusions in the traffic environment by sharing sensor data among collaborative vehicles (CoV) via vehicle-to-everything (V2X) network. With limited wireless bandwidth, CP necessitates task-oriented and receiver-aware sensor scheduling to prioritize important and complementary sensor data. However, due to vehicular mobility, it is challenging and costly to obtain the up-to-date perception topology, i.e., whether a combination of CoVs can jointly detect an object. In this paper, we propose a combinatorial mobility-aware sensor scheduling (C-MASS) framework for CP with minimal communication overhead. Specifically, detections are replayed with sensor data from individual CoVs and pairs of CoVs to maintain an empirical perception topology up to the second order, which approximately represents the complete perception topology. A hybrid greedy algorithm is then proposed to solve a variant of the budgeted maximum coverage problem with a worst-case performance guarantee. The C-MASS scheduling algorithm adapts the greedy algorithm by incorporating the topological uncertainty and the unexplored time of CoVs to balance exploration and exploitation, addressing the mobility challenge. Extensive numerical experiments demonstrate the near-optimality of the proposed C-MASS framework in both edge-assisted and distributed CP configurations. The weighted recall improvements over object-level CP are 5.8% and 4.2%, respectively. Compared to distance-based and area-based greedy heuristics, the gaps to the offline optimal solutions are reduced by up to 75% and 71%, respectively.

翻译：协同感知（CP）通过车联网（V2X）在协作车辆（CoV）间共享传感器数据，已成为解决交通环境中遮挡问题的有效方案。在无线带宽受限条件下，CP需要采用面向任务且接收端感知的传感器调度机制，以优先传输重要且互补的传感器数据。然而，由于车辆移动性，实时获取感知拓扑（即判断特定CoV组合能否联合检测到目标物体）具有挑战性且成本高昂。本文提出一种通信开销最小的组合式移动感知传感器调度（C-MASS）框架。具体而言，通过回放单个CoV及CoV对的传感器检测数据，构建最高至二阶的经验感知拓扑，从而近似表征完整感知拓扑。随后提出一种混合贪心算法，用于求解预算约束最大覆盖问题的变体，该算法具有最坏情况性能保证。C-MASS调度算法通过融合拓扑不确定性与CoV未探索时间对贪心算法进行自适应调整，以平衡探索与利用，从而应对移动性挑战。大量数值实验表明，所提C-MASS框架在边缘辅助与分布式CP配置中均接近最优解。相较于物体级CP方案，加权召回率分别提升5.8%与4.2%。与基于距离和基于面积的贪心启发式算法相比，与离线最优解的差距分别最高降低75%与71%。