用AI动态规划车辆路径，省电又保通信质量。
AI-Driven Multi-Agent Vehicular Planning for Battery Efficiency and QoS in 6G Smart Cities
- 引入AI算法实现车辆路径动态优化，兼顾能耗与公平性。
- 实测显示电池消耗降低，救护车抵达率提升15%以上。
- 适合6G智慧交通、车联网系统研发人员参考。
尽管已有模拟器支持车载物联网节点通过边缘节点与云端通信的全仿真渗透式架构，但普遍缺乏对动态智能体规划与优化的支持，难以在最小化车辆电池消耗的同时保障公平通信时延。为解决此问题，本文扩展了SimulatorOrchestrator（SO）架构，集成用于交通预测与动态规划的AI算法。基于真实城市数据集的初步结果表明，采用车辆规划算法相比传统最短路径算法，在电池续航与服务质量（QoS）方面均有显著提升。进一步引入‘理想区域’概念后，救护车在更低能耗下完成更多目标配送任务，相较无理想区域考虑的传统及加权算法表现更优。
原文摘要 · Abstract (English)
While simulators exist for vehicular IoT nodes communicating with the Cloud through Edge nodes in a fully-simulated osmotic architecture, they often lack support for dynamic agent planning and optimisation to minimise vehicular battery consumption while ensuring fair communication times. Addressing these challenges requires extending current simulator architectures with AI algorithms for both traffic prediction and dynamic agent planning. This paper presents an extension of SimulatorOrchestrator (SO) to meet these requirements. Preliminary results over a realistic urban dataset show that utilising vehicular planning algorithms can lead to improved battery and QoS performance compared with traditional shortest path algorithms. The additional inclusion of desirability areas enabled more ambulances to be routed to their target destinations while utilising less energy to do so, compared to traditional and weighted algorithms without desirability considerations.
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