On a closed test track, the performance of most ADAS and AV systems appears ready for the market. Lane lines are crisp, detections fire cleanly and braking feels predictable. Real roads and the real world behave differently. Glare hits from unexpected angles. Occlusions hide objects until the last moment. Lens soiling builds gradually. Rain and snow reshape contrast, shadows and lane cues. And most importantly, other road users do not behave as predictably as we would expect them to. None of this is exotic - it is everyday driving.

Regulators are explicitly acknowledging this gap. Euro NCAP’s upcoming 2026 protocols introduce expanded, real-world representative test scenarios, because current standardized tests do not fully capture the environments where ADAS systems actually operate.

That is where engineering maturity is exposed: not by how a system behaves in ideal scenes, but by whether its behavior stays predictable as conditions drift, overlap and degrade over time.

This is what Key Performance Indicators (KPIs) measure - but only when treated as evidence, not dashboard statistics. A number without context is not a KPI. It is a claim waiting to be challenged.

This article outlines a practical, engineering-grade KPI framework used by modern ADAS/AV teams. It is built around three dimensions necessary for trustworthy deployment:

• Performance: how the system behaves
• Coverage: where and under which conditions it was tested
• Readiness: whether behavior remains stable enough to release.

It also explains how KPIs become a repeatable evidence pipeline, not a one-off report and why real-world validation is a non-negotiable part of ADAS validation and workflows and AV testing.