AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration

2025-06-03

AI Summary

To address real-time multi-anomaly diagnosis for intelligent connected vehicles under high-mobility scenarios, this paper proposes a lightweight edge AI service system that tackles service migration challenges induced by dynamic network handovers. Methodologically, we design a closed-loop service orchestration framework that innovatively incorporates cellular-level network metrics—namely Reference Signal Received Power (RSRP) and network slice load—into a Quality-of-Experience (QoE)-driven migration decision mechanism. The system integrates 5G network awareness, a lightweight CNN-LSTM anomaly detection model, and service mesh-based orchestration. Evaluated on a real-world 5G racetrack testbed, the system achieves end-to-end inference latency ≤85 ms, service migration success rate ≥99.2%, and anomaly detection F1-score of 0.93—representing a 12.7% improvement over baseline methods. Our core contribution lies in the joint optimization of ultra-low-latency inference, high-accuracy service migration, and real-time anomaly diagnosis.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Real-time AI diagnostics for vehicle anomalies
• Dynamic edge service migration for mobility
• Low-latency AI inference in 5G environments

Innovation

• AI-driven real-time vehicle anomaly diagnostics
• Dynamic edge service migration for mobility
• Closed-loop orchestration with 5G network metrics