Perception Pipeline Validation Strategy

Make perception performance measurable, and defensible

Perception breaks in the corners: rare scenarios, domain shifts, sensor artifacts, and silent degradations. I help teams design a validation strategy that connects engineering reality with ISO 26262 and SOTIF expectations: clear performance claims, measurable acceptance criteria, scenario-driven coverage, and evidence that stands up in reviews, due diligence, and audits.

I translate "it works well" into explicit performance claims: what the perception stack detects, under which ODD assumptions, and where it fails. Then we build a validation plan covering datasets, ground-truth strategy, metrics, scenario catalog, and regression gates, aligned with ISO 26262 and SOTIF evidence expectations and practical CI/CD constraints.

A practical validation playbook for perception systems

1. Performance claims and acceptance criteria. Define measurable detection, track, and localization claims per ODD, including thresholds, confidence handling, and failure boundaries.
2. Scenario catalog and coverage model. Build a scenario taxonomy covering weather, illumination, occlusions, and edge cases, and link it to requirements and test evidence.
3. Dataset and ground-truth strategy. Recommend data sources, sampling, labeling and ground-truth approach, and bias checks to ensure representativeness and traceability.
4. Robustness and degradation testing. Plan stress tests for sensor artifacts, domain shifts, adversarial-like perturbations, and silent failure detection.
5. Validation pipeline and regression gates. Define continuous evaluation, dashboards, release gates, and how to prevent metric gaming and drift over time.
6. Safety argument and evidence packaging. Structure the results into reviewable evidence: what is proven, what is assumed, residual risk, and mitigation rationale.

How we work

1. System intake and ODD framing. Understand sensors, stack boundaries, ODD assumptions, and target claims.
2. Metrics, datasets, and scenario model. Define KPIs, scenario taxonomy, and which datasets and ground-truth sources are required for defensible results.
3. Test plan and pipeline design. Build a validation pipeline with regression gates, drift monitoring, and release criteria tied to requirements.
4. Evidence packaging and review readiness. Produce an audit-friendly evidence package: claims, coverage, results, gaps, and prioritized next actions.