Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, a vehicular edge computing (VEC) architecture is presented, in which heterogeneous information can be cooperatively sensed and uploaded via Vehicle-to-Infrastructure (V2I) communications. Logical views can be constructed by fusing the heterogeneous information at edge nodes. Further, we derive a cooperative sensing model based on the multi-class M/G/1 priority queue. On this basis, we define the AoV by modeling the timeliness, completeness, and consistency of the logical views. Then, we formulate the problem, which aims at maximizing the quality of VCPS. Further, a multi-agent deep reinforcement learning solution is proposed. The system state includes vehicle sensed information, edge cached information, and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information, and the edge action space is the V2I bandwidth allocation. The system reward is defined as the achieved VCPS quality. In particular, a difference-reward-based credit assignment is designed to divide the system reward into the difference reward for vehicles, reflecting their individual contributions. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution.
翻译:合作遥感和多种信息融合对于实现视频网络物理系统(VCPS)至关重要。本文件首次尝试通过设计名为 " 视觉时代(AoV) " 的新度量 VCPS的质量。具体地说,我们提出了一种名为 " 视觉时代(AoV) " (AoV)的新度来定量测量VCPS的质量。具体地说,提出了一种车辆边缘(V2I)结构结构,在这个结构中,可以合作感知各种信息并通过车辆对基础设施(V2I)的通信(V2I)进行上传。逻辑观点可以通过在边缘节点使用混杂信息来构建。此外,我们根据多级M/G/1优先排队列制作了一个合作感知模型。在此基础上,我们通过模拟逻辑观点的及时性、完整性和一致性来定义AOVV值质量。然后,我们制定了一个问题,目的是最大限度地提高VCP的质量。此外,提出了一种多剂深度强化学习解决方案。系统状态包括车辆感知信息、边缘模型和视觉要求。车辆行动空间包括感测频率和上的信息优先级,而边缘行动空间是V2I的等级空间。 边缘行动空间是V2I 带宽值分配的差别。我们为最终奖分级分配。我们所设计的奖得分。