Most ADAS/AV programs already have KPI frameworks in place. Sensor metrics are tracked, function performance is monitored, feature KPIs are reviewed, and system-level summaries show up regularly in release discussions. On paper, validation often appears complete and methodical.

However, the moments that matter most - replay triage, release gates, post-release investigations - still surface behavior that doesn’t match what a green dashboard suggests. The gap is rarely “we have no metrics.” More often, the gap is quieter: KPI results get treated as if they carry the same meaning at every layer, even though the meaning changes as signals cross boundaries.

This article focuses on the structural reasons behind that mismatch: how KPI layers interact, how instability creeps upward from sensors through functions and features to system behavior, and why the interfaces between KPI layers are where confidence most often thins out. That’s also where teams eventually learn why test-track performance alone fails as real-world evidence.