SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions

2025-07-25

AI Summary

To address the challenge of inefficient root-cause localization in software-defined vehicles (SDVs) under cloud-edge collaborative architectures—where complex system dependencies hinder automated fault diagnosis and impede manual analysis—this paper proposes a modular, automated diagnostic platform. The platform integrates dynamic dependency graph modeling with real-time anomaly monitoring, enabling runtime module replacement and adaptive dependency updates. It constructs anomaly-annotated snapshot graphs via streaming collection of system metrics and employs graph traversal to rank potential root causes by probability. Key technical contributions include a cloud-edge cooperative diagnostic architecture, dynamic graph modeling techniques, a modular diagnostic pipeline, and novel graph-based analytical algorithms. Evaluated on 5G-connected physical vehicles, the platform reliably detects injected faults, reduces mean time to localization by 62%, and significantly enhances system availability and early defect detection capability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Connected and software-defined vehicles promise to offer a broad range of services and advanced functions to customers, aiming to increase passenger comfort and support autonomous driving capabilities. Due to the high reliability and availability requirements of connected vehicles, it is crucial to resolve any occurring failures quickly. To achieve this however, a complex cloud/edge architecture with a mesh of dependencies must be navigated to diagnose the responsible root cause. As such, manual analyses become unfeasible since they would significantly delay the troubleshooting. To address this challenge, this paper presents SDVDiag, an extensible platform for the automated diagnosis of connected vehicle functions. The platform enables the creation of pipelines that cover all steps from initial data collection to the tracing of potential root causes. In addition, SDVDiag supports self-adaptive behavior by the ability to exchange modules at runtime. Dependencies between functions are detected and continuously updated, resulting in a dynamic graph view of the system. In addition, vital system metrics are monitored for anomalies. Whenever an incident is investigated, a snapshot of the graph is taken and augmented by relevant anomalies. Finally, the analysis is performed by traversing the graph and creating a ranking of the most likely causes. To evaluate the platform, it is deployed inside an 5G test fleet environment for connected vehicle functions. The results show that injected faults can be detected reliably. As such, the platform offers the potential to gain new insights and reduce downtime by identifying problems and their causes at an early stage.

Problem

• Automated diagnosis of connected vehicle functions
• Dynamic dependency tracking for root cause analysis
• Early fault detection to reduce vehicle downtime

Innovation

• Modular platform for automated vehicle diagnosis
• Dynamic graph view for dependency tracking
• Self-adaptive runtime module exchange capability