The integration of Artificial Intelligence with the Internet of Things (AIoT) significantly enhances the functionality of Vehicle-Road Cooperation (VRC) systems by enabling smarter, real-time decision-making and resource optimization across interconnected vehicular networks. To tackle the challenges associated with resource constraints, this study introduces a method where vehicle users can offload tasks to nearby Roadside Units (RSUs) or service-oriented vehicles to ensure timely application execution. However, this task offloading introduces additional transmission delays and energy expenditures. Consequently, the paper first conceptualizes the computation offloading problem, aiming to minimize the total task processing time and energy consumption under the constraints of resources provided by RSUs and service-oriented vehicles. We model the computation offloading issue within the VRC framework as a Markov Decision Process (MDP) and propose a multi-agent reinforcement learning-based resource scheduling method. Each vehicle, acting as an intelligent agent, interacts with and influences decisions within this environment. The method integrates the Twin Delayed Deep Deterministic policy gradient algorithm to train deep neural networks for deciding on task offloading and computational resource allocation. Simulation results demonstrate that compared to existing algorithms, the proposed method more effectively utilizes the computational resources available through RSUs and service-oriented vehicles within the VRC system. It achieves joint optimization of latency and energy consumption, thus validating the efficacy of the proposed approach in enhancing the operational efficiency and sustainability of urban transportation systems.

中文翻译：

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通过AIoT实现高效车路协作：计算卸载的深度学习方法


人工智能与物联网（AIoT）的集成通过在互联车辆网络上实现更智能、实时的决策和资源优化，显着增强了车路协作（VRC）系统的功能。为了解决与资源限制相关的挑战，本研究引入了一种方法，车辆用户可以将任务卸载到附近的路边单元（RSU）或面向服务的车辆，以确保及时执行应用程序。然而，这种任务卸载会带来额外的传输延迟和能量消耗。因此，本文首先概念化了计算卸载问题，旨在在 RSU 和面向服务的车辆提供的资源约束下最小化总任务处理时间和能耗。我们将 VRC 框架内的计算卸载问题建模为马尔可夫决策过程（MDP），并提出一种基于多智能体强化学习的资源调度方法。每辆车都充当智能代理，与该环境中的决策进行交互并影响决策。该方法集成了双延迟深度确定性策略梯度算法来训练深度神经网络，以决定任务卸载和计算资源分配。仿真结果表明，与现有算法相比，所提出的方法更有效地利用 VRC 系统内 RSU 和面向服务的车辆提供的计算资源。它实现了延迟和能耗的联合优化，从而验证了所提出的方法在提高城市交通系统的运营效率和可持续性方面的有效性。