The system fails
A resistor drifts, a bit flips in RAM, a task overruns its deadline. Classic malfunctioning behaviour of an E/E system — random hardware faults and systematic faults in hardware and software.
Functional safety · SOTIF · Safe AI
We bridge ISO 26262, ISO 21448 (SOTIF) and ISO/PAS 8800 into a single, traceable safety case — so your automated driving function is not only engineered safely, but demonstrably approvable.
The problem
Each of the three standards is complete on its own terms — and blind outside them. Treat them as three separate audits and you will pass all three while the residual risk sits in the seams between them.
A resistor drifts, a bit flips in RAM, a task overruns its deadline. Classic malfunctioning behaviour of an E/E system — random hardware faults and systematic faults in hardware and software.
Every component works exactly as specified, and the vehicle still behaves unsafely: low sun blinds the camera, radar loses a stationary object, the specification never anticipated the scenario. Functional insufficiency, not fault.
The insufficiency is not in the code — it is in the data and the model. Distributional shift, an unrepresentative training set, brittle generalisation, and a decision boundary nobody can inspect line by line.
The seam is the risk. A perception stack can meet every ASIL D hardware architectural metric and still fail to classify a stationary vehicle at closing speed. ISO 26262 records a pass. SOTIF records a hazard. ISO/PAS 8800 explains why the model was always going to miss it. Only an argument that spans all three can close the case.
What we do
One hazard. Three families of cause. One safety case that traces every hazard through every applicable framework to evidence — and states the residual risk in language an assessor and an approval authority will accept.
The item is faulted. Answered with ASIL classification, safety mechanisms, diagnostic coverage and hardware metrics.
The item is fault-free but not capable enough. Answered with triggering conditions, scenario coverage and acceptance criteria.
The insufficiency originates in data, training or generalisation. Answered with an AI safety lifecycle and model-level evidence.
Services
Engage one where you have a specific gap, or all three where you need a complete argument. Either way the work product is designed to slot into the same safety case.
01 / Functional safety
Item definition, HARA and ASIL determination, functional and technical safety concepts, hardware metrics, software safety requirements, confirmation measures and the safety case.
02 / Intended functionality
Triggering condition analysis, the known/unknown × safe/unsafe scenario space, quantitative acceptance criteria, scenario-based V&V strategy and field monitoring.
03 / Artificial intelligence
AI safety lifecycle, dataset specification and coverage, model-level safety requirements, robustness and out-of-distribution evidence, runtime monitoring and the AI safety argument.
Engagement
Consulting that produces artefacts, not slideware. Every engagement ends with something you can hand to an assessor, a customer or the KBA.
Gap and readiness assessment against all applicable frameworks at once. You get a prioritised finding list mapped to clauses, not a generic maturity score.
Safety goals, decomposition and the allocation of requirements across functional safety, SOTIF and AI evidence — so the architecture carries the argument rather than fighting it.
A V&V strategy that says what each test proves and what residual claim survives it: scenario catalogues, coverage rationale, dataset specifications, metrics and thresholds.
A structured safety case — goals, strategies, context, evidence — with a defensible residual risk statement, plus support through independent assessment and approval.
Regulatory readiness
Standards conformance is necessary but not sufficient. We shape the technical evidence so it maps directly onto the regulatory instrument you actually have to satisfy.
Germany's autonomous vehicle approval and operation ordinance for SAE Level 4 in a defined operational area: technical supervision, risk-minimal state, data recording and the KBA dossier.
Type approval for automated lane keeping and driver control assistance systems, including the audit and reporting scheme and the scenario catalogue expectations.
Cybersecurity and software update management systems — the security case that a modern safety case can no longer be written without.
Where safety-relevant AI meets horizontal AI regulation: risk management, data governance, logging, transparency and human oversight obligations.
Business-level fluency
About
Build your trustworthy system with me
safeautonomy is an independent consultancy for the safety of automated and AI-driven vehicle functions. We work with OEMs, Tier 1 suppliers and automated-driving developers at exactly the point where functional safety, SOTIF and machine learning stop being separate disciplines.
Where we work
Headquartered in Wolfsburg — home to Volkswagen and a dense cluster of OEM and Tier 1 engineering teams — and working with clients well beyond it, on site wherever a programme needs us, not just reviewing documents from a distance.
Headquartered in Lower Saxony's automotive engineering hub.
Remote-first engagements across the EU, North America and Asia.
Get in touch
A 30-minute call is usually enough to tell whether your gap is in functional safety, in SOTIF, in the AI evidence — or in the traceability between them.
Phone / WhatsApp
+352 661 553 282Based in
Wolfsburg, Germany — working worldwide