Cloud Diagnostics Enable Proactive SDV Troubleshooting

In their article on cloud-based diagnostic services, Guido Rulhof and Rhys Evans describe how a data-driven diagnostic infrastructure can transform troubleshooting for software-defined vehicles by linking four interdependent technical components.

From reactive troubleshooting to data-driven diagnostics
Diagnosing software-defined vehicles (SDVs) requires a shift away from reactive fault analysis toward proactive, continuous processes. A core prerequisite for this transition is over-the-air (OTA) capability, which enables remote access to diagnostic data as well as remote fault resolution. In parallel, SDVs require a software-centric diagnostic interface. The introduction of SOVD (Service-Oriented Vehicle Diagnostics) provides this foundation and enables diagnostics to be embedded into a cloud-based, data-driven infrastructure.

Within this framework, troubleshooting becomes a continuous process in which diagnostic data, software updates and vehicle health insights are closely interlinked.

Modular, SOVD-capable cloud diagnostics
A central element of this approach is a modular, SOVD-capable diagnostic tool that operates in the cloud. The Cloud-Hosted Diagnostics Solution from ETAS allows client-side applications to directly access current diagnostic data and diagnostic sequences that are centrally managed in the cloud. Its backbone architecture supports the integration of existing OEM systems and enables adaptation to different operational and business models. This modularity allows diagnostics to scale across vehicle fleets while remaining compatible with heterogeneous tool landscapes.

Intuitive authoring of diagnostic content
The quality and efficiency of future diagnostics depend on how easily complex diagnostic procedures can be created and maintained. Intuitive authoring solutions address this requirement by allowing diagnostic experts to configure routines without extensive software engineering expertise. Implemented through an integrated development environment (IDE), these tools enable OEMs, suppliers and service providers to define and adapt diagnostic logic in a data-driven manner, supporting rapid updates throughout the vehicle lifecycle.

Cloud-based simulation and validation
Another key component is cloud-based simulation and validation of diagnostic applications. By executing diagnostic software in a virtual environment, developers can test and validate diagnostic logic without access to physical vehicles or hardware-in-the-loop systems. This approach reduces development time and costs while enabling earlier and more comprehensive testing. As a result, diagnostic quality improves before deployment to vehicles in the field.

Vehicle health monitoring and predictive maintenance
Cloud-based vehicle health monitoring completes the diagnostic framework. ETAS Vehicle Analytics and Intelligence uses machine learning methods and knowledge graphs to analyze operational data, diagnostic information and software logs from vehicles. This enables the identification of complex fault patterns and correlations, providing technicians with precise diagnoses and data-driven recommendations. These insights also support predictive maintenance, as potential failures can be detected early and, where possible, resolved via firmware-over-the-air updates.

Diagnostic services for the SDV lifecycle
As SDVs become more complex and software-intensive, isolated fault analysis is no longer sufficient. Proactive, cloud-based diagnostic services provide a future-proof approach by combining agile creation of diagnostic content, cloud-based simulation and flexible execution in the field. Together with continuous vehicle health monitoring, this integrated model enables OEMs and fleet operators to improve troubleshooting efficiency, maximize vehicle availability and support higher levels of customer satisfaction across the entire vehicle lifecycle.

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