NADIR field guide

Clean Scan, Wrong Alignment: Why ADAS Verification Needs Shadow Scoring

A passed scan or green dashboard does not prove post-repair ADAS alignment. Why fleets need shadow residual scoring after glass and body work.

01 / INTERACTIVE DIAGRAM

Post-repair detection path

Repair import → ingest → score → queue → evidence — illustrative seed data.

NADIR VISUAL · SEED DATAILLUSTRATIVE · NOT FIELD PERFORMANCE
Nominal Caution Critical

Article companion visual · not field performance.

02 / DETAILS

Read this article two ways

Plain language for fleet and investor conversations. Technical detail for engineering diligence.

Plain-language takeaway

This guide supports NADIR's detection-first motion: find likely post-repair miscalibration using telematics you already export.

Shadow mode means tiers and signed evidence — not automatic vehicle changes or ECU writes.

  • Pair reading with a 4-week $750 shadow proof if you're evaluating vendors.
  • Close pilots on MTTFF-RE — median flag within seven days on ≥70% of repair events.
03 / DETAILS

Article FAQ

Quick answers for fleet and engineering readers.

Article FAQ

Quick answers for fleet and engineering readers.

After windshield replacement or front-end body work, shops often return a vehicle with a clean scan report and a green OEM health dashboard. That is not the same as a calibrated ADAS stack under real driving residuals. Fleets need a shadow scoring layer that compares telematics you already export against repair-event context — without ECU writes — before vehicles return to revenue service.

The gap between scan pass and road truth

Static target routines prove the scan tool communicated with ECUs. They do not continuously prove camera-to-vehicle extrinsics, radar boresight, or fusion consistency across temperature, load, and vibration cycles. A vehicle can pass in bay lighting and drift within the first hundred revenue miles.

Fleet safety teams see this as liability exposure: the repair record says calibrated, the telematics story says nominal, and the first near-miss event has no signed evidence chain tying repair timing to residual elevation.

Why dashboards lie quietly

OEM and telematics dashboards aggregate health at coarse intervals. Missing calibration events, partial procedures, or skipped dynamic steps often surface only as subtle residual elevation — not as a hard fault code. Without cross-modal scoring, a forward camera can be several tenths of a degree off while lane-keeping and AEB still appear functional until edge cases stack.

That is the core detection problem NADIR pilots target: find miscalibration after repair before it becomes a dispatch incident or insurer dispute.

Shadow scoring after repair events

Shadow mode ingests batch telematics (CSV, JSONL, or API), joins repair order timestamps, and scores Pulse-tier residuals per VIN. CRITICAL rows enter a Console queue; signed evidence bundles capture model version, source signals, and tier transitions for audit — with no automated dispatch holds until stakeholders trust alert rates.

The operational contract is simple: every post-repair VIN gets a detection window; MTTFF-RE measures how fast CRITICAL flags appear after the RO close timestamp.

What fleets should require from partners

  • Repair-event feed or weekly RO CSV with VIN and close time
  • Telematics export cadence that covers the first seven days back on route
  • Shadow-only pilot language in LOI — advisory analytics, not ASIL, no ECU writes
  • Week-4 readout with MTTFF-RE distribution, not just tier histograms

Next steps

Start with the windshield recalibration guide, watch the product walkthrough, then scope a 4-week shadow pilot on 50–150 ADAS-equipped vehicles.

Close the loop with MTTFF-RE.

Run a 4-week shadow pilot on 50–150 ADAS vehicles and export signed evidence.