Automotive

SOVD – Diagnostic Standard for Software-Defined Vehicle (SDV)

Rethinking Diagnostics – Local, Remote, and Future-Ready

Service-Oriented Vehicle Diagnostics (SOVD) is a key enabler for the next generation of diagnostics in software-defined vehicles (SDVs). As vehicle architectures become more centralized, IP-based, and complex, traditional diagnostic protocols like UDS or DoIP increasingly reach their limits.

SOVD, developed as part of an ASAM standard, aligns diagnostic processes with modern IT technologies.
Instead of proprietary communication stacks, SOVD relies on REST APIs, JSON or HTTP/2 – enabling direct, standardized access to vehicle functions, whether locally, remotely, or via the cloud.

Whether in production, in the workshop, or from the cloud, whether for end-of-line testing, app-based workshop access, or remote diagnostics over-the-air (OTA) – SOVD provides a unified interface for diagnostics across all phases of the vehicle lifecycle.

Key benefits of SOVD at a glance:

  • Real-time remote access to vehicle data (e.g., via OTA diagnostics or fleet monitoring)
  • Seamless integration into existing development and test environments
  • Future-proof architecture through IP-based cloud connectivity
  • Platform-independent use, including mobile access

The recent HANSER automotive article (April 2025) describes SOVD as a foundational technology for diagnostic capability in SDVs, supported by real-world use cases from development to after-sales. The ASAM Guide Online also highlights how SOVD bridges the gap between classic and cloud-based diagnostics.

To learn more about the technology behind SOVD and its practical applications, take a closer look at our published technical articles on SOVD.


Webinar

Keynotes

  • Introduction to the “SOVD” diagnostic standard 
  • Basic concepts and technical boundary conditions 
  • Service-oriented diagnostics in typical use cases
  • Advantages and disadvantages of the new standard 

Wednesday, Januar 16, 2025
(Recording available)

Englisch

Webinar


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