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, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, 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. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution.

翻译：协同感知与异构信息融合是实现车载信息物理系统（VCPS）的关键。本文首次提出一种名为“视角时效性”（Age of View, AoV）的新指标，以量化VCPS质量。具体而言，我们首先提出系统架构，该架构通过车载边缘计算（VEC）中的车对基础设施（V2I）通信实现异构信息的协同感知与上传。在边缘节点通过融合异构信息构建逻辑视图。进一步，我们基于多类M/G/1优先级队列推导协同感知模型来建立问题，并通过建模逻辑视图的时效性、完整性与一致性定义AoV。在此基础上，提出一种基于多智能体深度强化学习的解决方案。其中，系统状态包括车辆感知信息、边缘缓存信息及视图需求；车辆动作空间由感知频率与信息上传优先级构成；设计基于差异奖励的信用分配机制，将定义为VCPS质量的系统奖励分解为车辆对应的差异奖励。边缘节点基于预测的车辆轨迹与视图需求为车辆分配V2I带宽。最后构建仿真模型并进行全面性能评估，结果充分证明了所提方案的优越性。