The Software-Defined Vehicle Has A Documentation Problem

The software-defined vehicle pitch is simple enough to say out loud: the car becomes a platform, features ship like software, updates land over the air, the vehicle improves after purchase. On the engineering side, that pitch turns into a long list of moving parts, and what holds those parts together is not code alone. It’s documentation. Requirements, safety cases, change logs, update records, interface definitions, verification evidence, cybersecurity artifacts, supplier assumptions. The paperwork is not busywork, it’s the connective tissue.

A lot of the industry conversation about SDVs circles around architecture, compute, and org structure, but underneath it is a quieter problem: the documentation systems most automakers have were built for a different world, a world where the software footprint was smaller and change was slower. SDVs break that assumption.

Why SDVs raise the documentation stakes

In a traditional vehicle program, you can often treat documentation as something you finalize near the end, once the hardware and software settle. With SDVs, “settle” is not the right verb. The vehicle is maintained across its lifecycle, and the software is expected to change repeatedly. The car is not done when it ships, which means the evidence trail cannot stop when production starts either.

Regulators have started to codify that reality. UNECE’s software update regulation (UN Regulation No. 156) requires manufacturers to have a Software Update Management System (SUMS) and documented processes for software updates, including documentation describing updates such as their purpose and what systems or functions are affected.

That’s not just a compliance checkbox. It’s a forcing function. If you cannot explain what changed, why it changed, what it touches, and how you know it’s safe, you cannot operate the “ship updates continuously” model at scale.

Toyota’s SDV transition and the documentation gap

One of the clearest public examples of how hard this problem is comes from Toyota Motor Corporation.

As Toyota began shifting toward a more software-defined vehicle model, it invested heavily in building a unified vehicle software platform intended to standardize development, improve coordination with suppliers, and support long-lived software across vehicle programs. Public reporting around Toyota’s Arene platform has made it clear that this transition has been slower and more complex than originally expected, even for a company with deep engineering discipline and decades of process maturity.

What’s notable is not that Toyota struggled with software quality or talent. It’s that aligning a growing software footprint across teams, suppliers, and vehicle lifecycles exposed how difficult it is to keep assumptions, interfaces, and system behavior consistently documented as software becomes more central to the vehicle.

When a platform is expected to support multiple vehicle programs, evolve over time, and integrate with supplier software, documentation stops being a static artifact. It becomes part of the operating system of the organization. The challenge Toyota and other legacy OEMs face isn’t just writing that documentation once. It’s keeping it synchronized with reality as software updates, configurations, and dependencies change across years rather than model cycles.

This is the quiet friction underneath many SDV transitions. Even when the software roadmap is clear, the supporting artifacts that explain what the software does, how it’s used, and how it’s allowed to change are much harder to keep current. That gap doesn’t always show up as a headline failure. More often, it shows up as slowed rollout, delayed decisions, and extra coordination work that grows as the software surface area expands.

The real problem is not writing docs, it’s keeping them true

Engineering teams don’t struggle because they hate documentation. They struggle because SDVs multiply the number of places where truth can diverge.

A requirement lives in one system. The interface contract lives in another. The supplier delivers a component with its own assumptions. A team patches a bug, then an OTA update changes behavior in a way the original safety analysis did not anticipate. Somebody updates the code, somebody else updates the test plan, and the documentation lags behind because the release train is moving.

When you zoom out, you can see the shape of the problem. SDVs increase the rate of change, they increase the number of dependencies, and they increase the number of stakeholders who need to agree on what the system is supposed to do. The documentation load rises even if everyone is “doing their job,” because the system itself is more dynamic.

Traceability becomes the bottleneck nobody wants to own

If you talk to people building SDVs, a recurring theme is traceability: being able to connect a requirement to design decisions to implementation to verification evidence, then keep that chain intact as the product evolves and suppliers change.

This is not theoretical. In January 2026, Design News summarized SDV development pain points from engineers, including long development cycles, debugging and testing challenges, and regulatory delays as major blockers. The percentages are telling, because they point at system-level friction, not a lack of talent or ideas.

On the research side, automotive requirements traceability is active enough that you’re seeing papers built around detecting missing links and improving traceability accuracy on real datasets, which is basically an academic way of saying “people keep losing the thread.”

In practice, traceability breaks for boring reasons. Tools don’t integrate cleanly across OEMs and suppliers. Naming conventions drift. Teams split work across multiple backlogs. A requirement changes late, and the downstream artifacts don’t all get updated because nobody can see every dependency.

It’s not that teams don’t care. It’s that the system makes it easy for truth to fragment.

Safety and security both add documentation weight, and SDVs carry both

SDVs don’t just ship features. They ship change into safety-critical environments.

Functional safety standards, cybersecurity standards, and type-approval rules all lean heavily on documented evidence. Even when teams are building responsibly, the documentation burden rises because the proof must follow the change.

For example, safety programs emphasize demonstrable traceability across the lifecycle, linking hazards and safety goals through design and verification, and supplier ecosystems make that harder, not easier.

On the regulatory side, WP.29 doesn’t only care that you can update software, it cares that you can manage updates and cybersecurity as lifecycle disciplines. That pushes organizations toward process documentation that is auditable, repeatable, and current, not just a one-time program artifact.

This is where “documentation problem” stops meaning “writers and wikis” and starts meaning “engineering operations.” If your change-management and evidence systems are weak, the SDV promise becomes fragile.

What professionals keep pointing at

When you read industry commentary, even when it’s framed as “SDV roadblocks” or “execution gaps,” the recurring issues are familiar: integration complexity, process inefficiency, and regulatory pressure. That combination reliably produces documentation strain, because documentation is where complex systems get reconciled, and where disagreements are supposed to be resolved before they ship.

The argument isn’t that SDVs need more documents. It’s that SDVs need documentation that behaves like software: versioned, reviewable, traceable, and connected to the development workflow instead of living off to the side.

What an SDV-capable documentation system starts to look like

I’m not going to pretend there’s a universal blueprint, because OEM environments vary wildly, and supplier ecosystems make “one system” more aspirational than real. But you can usually tell when a team is moving toward SDV-ready documentation because a few shifts happen.

They stop treating documentation as static deliverables and start treating it as operational infrastructure. Documentation gets tied to change control, and change control gets tied to evidence. Update records are not a separate compliance activity, they’re part of shipping. Interfaces have owners. Requirements aren’t “done” when they’re written, they’re done when their downstream effects are visible and verified.

If that sounds expensive, it is, but it’s expensive in the same way test automation is expensive: you pay upfront so you don’t pay forever in late-stage surprises.

And if you want a simple gut-check, here it is. If a team can’t answer “what changed, what did it touch, and how do we know it’s still safe” without assembling five people and three spreadsheets, they don’t have a documentation problem as a side issue. They have a scaling problem that SDVs will keep exposing, release after release.