With the development of sensing technologies, vehicle-to-everything (V2X) communications, edge computing paradigm, vehicular cyber-physical systems (VCPS) are emerging as the most fundamental platform for realizing future intelligent transportation systems (ITSs). In particular, the construction of logical views at the edge nodes based on heterogeneous information sensing and uploading are critical to the realization of VCPS. However, a higher-quality view in terms of timeliness and accuracy may require higher cost on sensing and uploading. In view of this, this paper is dedicated to striking a balance between the quality and the cost for constructing logical views of VCPS. Specifically, we first derive an information sensing model based on multi-class M/G/1 priority queue and a data uploading model based on reliability-guaranteed vehicle-to-infrastructure (V2I) communications. On this basis, we design two metrics, namely, age of view (AoV) and cost of view (CoV), simultaneously. Then, we formulate a bi-objective problem to maximize the AoV and minimize the CoV. Further, we propose a distributed distributional deep deterministic policy gradient (D4PG) solution to determine sensing information, frequency, uploading priority, transmission power, and V2I bandwidth. Finally, we build a simulation model and give a comprehensive performance evaluation, and the simulation results conclusively demonstrate the superiority of the proposed solution.

翻译：随着传感技术、车联网（V2X）通信、边缘计算范式的不断发展，车载信息物理系统（VCPS）正成为实现未来智能交通系统（ITS）的最基础平台。其中，基于异构信息感知与上传在边缘节点构建逻辑视图对VCPS的实现至关重要。然而，在时效性和准确性方面更高质量的视图可能需要更高的感知与上传成本。鉴于此，本文致力于在VCPS逻辑视图构建的质量与成本之间取得平衡。具体而言，我们首先推导出基于多类M/G/1优先队列的信息感知模型和基于可靠性保障的车-基础设施（V2I）通信的数据上传模型。在此基础上，我们同时设计了两个指标，即视图时效（AoV）和视图成本（CoV）。随后，我们构建了一个最大化AoV与最小化CoV的双目标问题。进一步地，我们提出了一种分布式分布深度确定性策略梯度（D4PG）解决方案，以确定感知信息、频率、上传优先级、发射功率和V2I带宽。最后，我们构建了仿真模型并给出了全面的性能评估，仿真结果最终证明了所提方案的优越性。