What we measure, how we measure it.

This page is intentionally non‑confidential. It describes a validation mindset: define KPIs, measure them under controlled conditions, then confirm in realistic scenes.

Core KPIs (examples)

Range & precision

Distance accuracy and stability

Targets: calibrated reflectors and diffuse panels
Metrics: bias, \(\sigma\), tail risk
Conditions: sunlight, fog/rain proxies, angle

Latency

Sensor-to-decision time

Frame interval and per‑ROI update cadence
End-to-end timing budget (sensor → compute)
Jitter and worst-case behavior

Coexistence

Interference resilience

Multi‑LiDAR traffic injection tests
False positives / dropouts / artifacts
Mitigation impact on throughput

Reporting (what “good” looks like)

Repeatability

Same setup → same outcome

We track calibration state, ambient conditions, and test scripts so results are comparable over time.

Traceability

From claim → test → evidence

Each externally stated KPI ties to a test method and a reporting artifact.

Honest bounds

Disclose constraints

We report where performance degrades (weather, reflectivity, speed) and how mitigations trade off.

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