Information acquisition from target perception represents the key enabling technology of the Internet of Automatic Vehicles (IoAV), which is essential for the decision-making and control operation of connected automatic vehicles (CAVs). Exploring target information involves multiple operations on data, e.g., wireless sensing (for data acquisition), communication (for data transmission), and computing (for data analysis), which all rely on the consumption of time-space-frequency-computing (TSFC) multi-domain resources. Due to the coupled resource sharing of sensing, communication, and computing procedures, the resource management of information-oriented IoAV is commonly formulated as a non-convex NP-hard problem. In this article, further combining the integrated sensing and communication (ISAC) and computing, we introduce the integrated sensing, communication, and computing (ISCC), wherein the TSFC resources are decoupled from the specific processes and shared universally among sensing, communication, and computing processes. Furthermore, the information-oriented resource trading platform (IRTP) is established, which transforms the problem of ISCC resource management into a resource-information substitution model. Finally, we embed the employment topology structure in IoAV into neural network architecture, taking advantage of the graph neural network (GNN) and multi-worker reinforcement learning, and propose the dynamic resource management strategy based on the asynchronous advantage GNN (A2GNN) algorithm, which can achieve the convergence both of information gain maximization and resource consumption minimization, realizing efficient information-oriented resource management.

翻译：从目标感知中获取信息是实现自动车辆互联网（IoAV）的关键使能技术，这对于网联自动车辆（CAVs）的决策制定和控制操作至关重要。探索目标信息涉及对数据的多种操作，例如无线感知（用于数据采集）、通信（用于数据传输）和计算（用于数据分析），这些操作均依赖于时空频计算（TSFC）多域资源的消耗。由于感知、通信和计算过程的资源耦合共享，面向信息化的IoAV资源管理通常被建模为非凸NP-hard问题。本文进一步将集成感知与通信（ISAC）与计算相结合，提出了集成感知、通信与计算（ISCC），其中TSFC资源从具体过程中解耦，并在感知、通信和计算过程中通用共享。此外，建立了面向信息化的资源交易平台（IRTP），将ISCC资源管理问题转化为资源-信息替代模型。最后，我们将IoAV中的雇佣拓扑结构嵌入神经网络架构，利用图神经网络（GNN）和多智能体强化学习，提出了基于异步优势GNN（A2GNN）算法的动态资源管理策略，该策略能够实现信息增益最大化和资源消耗最小化的收敛，从而实现高效的面向信息化的资源管理。