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Industrial Autonomy & Engineering
Industrial Autonomy & Engineering: Building autonomous enterprises where intelligence is embedded directly into the value chain and operating models
Highlights
- Diagnostics is evolving from traditional ECU-based fault detection to holistic software health monitoring, enabling deeper visibility into system behaviour, dependencies, and performance.
- A service-oriented, API-driven diagnostic model enables standardised access across in-vehicle, remote, and proximity scenarios, improving scalability, interoperability, and integration across vehicle and cloud ecosystems.
- Diagnostics is becoming a secure, lifecycle capability that enables proactive issue detection, supports OTA updates and compliance, and drives continuous improvement through real-time vehicle intelligence.
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Why SDVs need service-oriented diagnostics
Consider a vehicle that arrives at a service centre with a malfunctioning driver-assistance feature. The root cause is not a failed component, but a software version mismatch across multiple systems. Traditional diagnostics can identify the fault codes, but often struggle to explain how interconnected software services, cloud updates, and vehicle platforms contributed to the issue.
Vehicles are evolving from mechanical products with embedded electronics into intelligent, connected, and continuously upgradeable software platforms. Diagnostics can no longer remain limited to workshop activities focused mainly on electronic control units (ECUs) and diagnostic fault codes. It must become a real-time, software-aware, and service-oriented capability that provides standardised access to vehicle health, software status, and system insights across vehicle, cloud, engineering, and service ecosystems.
Traditional diagnostics worked well when vehicle functions were distributed across ECUs and software changed infrequently. But modern vehicle architectures are being reshaped by advanced computing systems and software platforms, cloud connectivity, and over-the-air (OTA) updates. This expands the diagnostic challenge from identifying hardware faults to understanding software health, dependencies, and update readiness.
A modern diagnostic foundation must evolve beyond fault detection to provide intelligent root-cause analysis, system-wide impact assessment, software update readiness evaluation, and actionable corrective recommendations.
How are diagnostics evolving beyond ECUs in SDVs
Conventional diagnostics is largely centered around ECUs. Each ECU has predefined checks, fault codes, and standard diagnostic routines. A technician connects a diagnostic tool, checks stored fault codes, runs tests, and performs repair procedures. This model remains valuable, but it is no longer sufficient for SDVs.
In an SDV, a customer-visible function may depend on multiple software systems working across ECUs, sensors, and connected systems. A feature malfunction may be caused not by a failed component but by a software version mismatch, a service startup issue, a configuration error, a resource limitation, a software platform issue, or a communication problem.
This is why the diagnostic target is changing. It is no longer only a physical ECU; it may also be a software function, application or software platform. As vehicle architecture becomes more centralised and software-driven, diagnostics must expand from fault-code retrieval to deeper visibility into software behaviour, system interactions, and overall system health.
Advanced central computing systems further accelerate this shift, serving as the vehicle's digital brain and running multiple applications simultaneously. Diagnostics must therefore evolve into having better visibility into how software systems perform, how they depend on each other, and whether they are working as expected.
What role does SOVD play in SDVs
A service-oriented approach applies modern software principles to vehicle diagnostics by exposing diagnostic capabilities through standardised APIs rather than isolated ECU interfaces. This makes diagnostics more accessible, scalable, and interoperable across vehicle, cloud, engineering, and service ecosystems.
Service Oriented Vehicle Diagnostics (SOVD) plays an important role in this transition. It supports access to diagnostic content for both ECUs and software-based systems and can be used across remote, proximity, and in-vehicle scenarios. By leveraging modern web-based technologies, SOVD simplifies integration with cloud platforms, service tools, engineering systems, and lifecycle management applications. In practice, where a technician once connected a proprietary tester to query one ECU at a time, the same standardised API can now retrieve fault and software-health data uniformly across ECUs and central computers - reducing tool sprawl, improving scalability, and speeding integration with cloud and engineering systems.
The transition will be evolutionary rather than disruptive. Future vehicles will continue to operate with a mix of traditional ECUs and centralised computing platforms, making coexistence between conventional diagnostics and SOVD-based approaches essential.
Why must diagnostics support the entire vehicle lifecycle
Diagnostics must support the entire vehicle lifecycle, from development and validation to manufacturing, aftersales, and field operations.
As software updates become central to lifecycle management, global regulations for software updates and cybersecurity (such as UN R155 and R156) reinforce the need for secure, traceable, and governed diagnostic capabilities.
As diagnostics become remote and digitally connected, it also becomes a cybersecurity-sensitive access layer. Diagnostic services can expose vehicle data, trigger routines, interact with software updates, or influence vehicle functions. A future-ready diagnostic architecture, therefore, requires robust security measures, including controlled access to safety-critical services, data protection, and monitoring.
From fault detection to continuous vehicle intelligence
The future of SDV diagnostics is not just fault detection - it is continuous vehicle health intelligence. By connecting vehicle data, software status, diagnostic insights, cloud-based analysis, and engineering feedback, OEMs can move from reactive repair to proactive issue resolution.
Continuous vehicle intelligence helps vehicles monitor their health, detect issues early, support predictive maintenance, and provide OEMs with real-world insights to improve software and vehicle performance.
For OEMs, this requires a shift in mindset. Diagnostics should not be treated as a late-stage service function. Instead, it must be designed as a strategic lifecycle capability that spans vehicle systems, software platforms, cloud connectivity, cybersecurity, and software update ecosystems, and that can provide continuous visibility into vehicle health and software performance throughout the lifecycle.
As the industry moves from SDVs to AI-Defined Vehicles (AIDVs), diagnostics will evolve from identifying issues to predicting and preventing them. While SDVs provide the digital foundation, AIDVs enable learning from data, anticipating outcomes, and continuously improving vehicle behaviour. Diagnostics will increasingly help vehicles become more adaptive, intelligent, self-healing and self-improving throughout their lifecycle.
Future diagnostic architectures will deliver deeper visibility into software behaviour, system interactions, update readiness, cybersecurity posture, and operational performance across connected ecosystems. The vehicles of the future will be defined by software, and their reliability will increasingly depend on how intelligently that software can be diagnosed, secured, updated, and improved. Diagnostics is no longer a support function; it is becoming the intelligence layer that enables vehicles to predict, prevent, and increasingly resolve issues autonomously, allowing them to continuously learn, improve, and operate with greater autonomy throughout their lifecycle.