Every perception. Every decision. Every action. Observable, queryable, and auditable. Finally answer the question regulators, lawyers, and engineers all ask: "Why did the car do that?"
This is the fundamental challenge of autonomous vehicles. When something goes wrong, nobody can trace the exact chain of perception → decision → action.
NHTSA investigations require explaining every decision. Without structured telemetry, teams spend months reconstructing what happened.
In litigation, "the AI decided" isn't a defense. You need to show exactly what the system perceived, decided, and why.
When the AI makes an unexpected decision, engineers can't query "show me all similar decisions" because events aren't structured.
Every autonomous driving decision becomes a structured, queryable event.
The same domain.entity.action:version pattern that works for code telemetry
works for vehicle telemetry.
NHTSA, NTSB, EU regulators all want the same thing: explain what happened. EventIDs give you a complete, queryable record of every decision.
In litigation, show the exact perception → decision → action chain. The AI isn't a black box anymore — it's an auditable system.
The AI can reason about its own decisions. "I made 47 yield decisions today. 3 were unnecessary." That's the feedback loop.
Query "all left-turn decisions where confidence < 0.8" across your entire fleet. Find edge cases in seconds, not months.
The Event Model enables continuous improvement by making every AI decision observable.
You can't improve what you can't measure.
The same Event Model that makes AI code observable can make autonomous systems auditable. Start with the specification, build with the playground.