Narayanasamy Iswarya, Rajamanickam Venkateswari
Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore 641004, India.
Sensors (Basel). 2024 Aug 30;24(17):5658. doi: 10.3390/s24175658.
The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.
由于能有效利用道路，汽车和卡车的编队行驶是自动驾驶的一种相关方法。由于可持续性，降低的油耗水平是一个额外的优点。传统的编队行驶依赖基于专用短程通信（DSRC）的车对车通信。计算由编队成员以其有限的能力执行。5G的出现有利于智能交通系统（ITS）在编队模式中采用多接入边缘计算（MEC），即将计算任务卸载到边缘服务器。在本研究中，对车辆编队系统中的重要参数，即对延迟敏感的无线电资源管理方案和信息年龄（AoI）进行了研究。此外，还研究了协作感知消息（CAM）在路边单元（RSU）快速接收安全关键消息的传输速率。然而，对于像车辆网络这样对延迟敏感的应用，解决多个相关目标至关重要。为了有效且同时地解决这些目标，多智能体深度确定性策略梯度（MADDPG）框架需要一个更好、更复杂的模型来增强其能力。本文提出了一种新颖的级联MADDPG框架CMADDPG，用于训练级联目标评论家，旨在通过智能体的协作行为实现预期奖励。在这种级联算法中，能有效规避阻碍系统整体性能的估计偏差现象。最终，实验分析还通过评估收敛因子证明了所提算法的潜力，该收敛因子能以最小失真快速稳定，且能以99%的概率可靠地传播CAM消息。平均AoI量保持在5 - 10毫秒范围内，保证了更好的服务质量。该技术已证明其在针对环境中更高移动性导致的信道不确定性进行分散式资源分配时的鲁棒性。最重要的是，所提算法的性能不受编队规模增加和主要信道不确定性的影响。
