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IoT and Digital Engineering
Bringing Together IoT and Digital Engineering to Create Rich Experiences
Highlights
- Software defined vehicles (SDVs) require scalable, cross-functional systems thinking to handle complex, connected vehicle ecosystems.
- GenAI and systems modeling language (SysML) v2 accelerate model-based systems engineering (MBSE) with faster modeling, seamless integration, and stronger return on investment.
- Embedding MBSE into core processes drives agility, end-to-end traceability, and continuous innovation beyond legacy limits.
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Why MBSE matters
The automotive industry is undergoing a paradigm shift driven by sustainability, automation, and user-centric innovation. As the vehicle ecosystem expands to include cloud, chargers, mobile apps, and GPS, interacting with off-board infrastructures, the vehicle is evolving into a software-defined entity.
This transformation makes the development lifecycle more complex. It brings together many stakeholders, including vehicle manufacturers (OEMs), tier-1s, semiconductor suppliers, over-the-air (OTA) software providers, and potential buyers. Each contributes unique requirements, which must be seamlessly integrated into the complete vehicle development cycle.
To stay competitive, OEMs must adopt MBSE practices that promote early integration of work products. MBSE enables holistic modeling of vehicle features, architectures, and dependencies, improving time to market, quality, and overall program efficiency. It provides a structured, system-level view essential for developing advanced capabilities like over-the-air updates and feature subscriptions. The time is now for the auto industry to embed MBSE into its core.
Roadblocks ahead
Despite recognizing the value of systems engineering, many OEMs struggle to implement MBSE effectively. Key reasons include:
- Limited awareness or hesitation toward adopting MBSE frameworks.
- Concerns over return-on-investment (ROI) due to upfront investments in training and tooling.
- Skilled engineers in languages like SysML and MBSE toolchains are in demand.
- The perceived overhead of modeling systems before implementation.
These hurdles create a gap between intent and execution. Organizations hesitate to leap without strong evidence of cost-benefit returns, missing out on long-term gains.
The shift with GenAI
When combined, emerging technologies like SysML v2 and generative AI can transform the MBSE landscape. SysML v2 introduces structured text notation that can be directly used to generate system diagrams, enabling efficient and interoperable system models. Its standardized interfaces reduce the risks associated with investing in a specific tool chain, developing tool-specific skills, and relying on individual tools.
Generative AI models can further accelerate this by auto-generating SysML v2 notations, significantly reducing modeling time and costs. When integrated with CI/CD pipelines, the generated SysML v2 notation fosters collaborative, agile development, seamlessly connecting typically siloed system models with the software development lifecycle (SDLC). This supports iterative feature delivery and builds end-to-end traceability, essential for SDV programs.
With these advances, MBSE will become more accessible, efficient, and future-proof, enabling OEMs to realize ROI faster.
What lies ahead
These innovations are being piloted in real-world SDV feature development. Comparisons between traditional MBSE approaches and GenAI-powered SysML v2 workflows within a cloud-based CI/CD setup reveal significant improvements in development speed, collaboration, and model reusability.
This methodology is not limited to the automotive domain. This new paradigm can benefit organizations that are hesitant to adopt MBSE due to ROI concerns. With the right combination of agile practices, modern tooling, and GenAI infusion, MBSE can evolve from a theoretical framework into a practical, business-critical asset.