AI驱动的车辆状态监测系统实现低延迟实时诊断与动态边缘迁移。
AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration
- 基于网络指标动态触发服务在边缘节点间迁移，应对车辆移动挑战。
- 5G环境下实测验证，确保低延迟推理与自适应服务部署。
- 适合智能交通与移动应用，兼顾实时性与实际部署可行性。
人工智能在车辆设备状态监测中应用日益广泛，旨在提升维护策略、降低成本并增强安全性。借助边缘计算范式，基于AI的状态监测系统可处理海量车辆数据流，实现异常检测与性能优化。本文提出一种新型车辆状态监测服务，在多样异常实时诊断的同时，具备在真实边缘环境中的实用性。为应对移动性挑战，我们设计了一种闭环服务编排框架，根据网络相关指标动态触发服务在边缘节点间的迁移。该方案已在具备5G能力的真实赛道环境中实现并测试，覆盖多种运行工况。实验结果表明，本框架能有效保障低延迟AI推理和自适应服务部署，展现出在智能交通与移动应用中的巨大潜力。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve safety. Leveraging the edge computing paradigm, AI-based condition monitoring systems process vast streams of vehicular data to detect anomalies and optimize operational performance. In this work, we introduce a novel vehicle condition monitoring service that enables real-time diagnostics of a diverse set of anomalies while remaining practical for deployment in real-world edge environments. To address mobility challenges, we propose a closed-loop service orchestration framework where service migration across edge nodes is dynamically triggered by network-related metrics. Our approach has been implemented and tested in a real-world race circuit environment equipped with 5G network capabilities under diverse operational conditions. Experimental results demonstrate the effectiveness of our framework in ensuring low-latency AI inference and adaptive service placement, highlighting its potential for intelligent transportation and mobility applications.
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