Multi-Sensor Autonomy Evidence Assurance
Review radar, camera, LiDAR and localization replay evidence before an autonomy scenario is promoted toward physical testing.
Who this is for
- Autonomy validation and release leads.
- Perception and sensor-fusion QA engineers.
- System-safety and integration leads responsible for evidence before vehicle testing.
The problem you already have
Healthy individual sensors can still disagree on the object that matters
Part 46 distinguishes sensor-level health from cross-sensor integrity. For this product, that supports a bounded replay review of whether radar, camera, LiDAR and localization evidence refer to the same scenario state closely enough for a customer-defined safety decision.
Timestamp drift and missing sectors can make a coherent-looking replay misleading
A replay package can contain individually plausible sensor outputs while one stream is stale, misaligned in time or incomplete across part of the field. Those defects can contaminate object association and localization evidence without being obvious in an aggregate KPI.
Offline sensor-fusion assurance must remain separate from live vehicle authority
The current evidence boundary is software-in-the-loop and offline replay only. BivectorAI does not control a live vehicle, request production credentials, require autonomy source code or claim HIL validation, road validation, production deployment or functional-safety certification.
Real-World Operational Scenario: The Replay Where Every Sensor Looked Healthy but They Disagreed on the Same Vehicle
Scenario: Camera, radar and LiDAR each report a plausible target, but their timestamps or object positions do not line up closely enough for the localization state used by the replay.
The validation team needs to decide whether the scenario can advance toward physical testing or should remain review-blocked.
WITHOUT this gate: individual sensor-health summaries can make the replay look clean while the cross-sensor contradiction survives into a later vehicle test.
WITH this gate: timestamp freshness, sector completeness, cross-sensor positional consistency, localization context and environmental degradation are reviewed together under one bounded evidence contract. The output is a scenario-level evidence decision; the customer still owns every physical-test and release decision.
What BivectorAI Provides
Cross-Sensor Agreement Review
Reviews whether supplied radar, camera and LiDAR observations are mutually consistent for the agreed replay scenario without treating any single sensor as automatically authoritative.
Timestamp & Localization Freshness Review
Surfaces stale or misaligned sensor/localization evidence that can weaken a scenario decision even when each source appears individually plausible.
Missing-Sector & Environmental-Degradation Screening
Uses Part 46 technical lineage to identify incomplete coverage and degradation context such as rain, fog or glare that should remain explicit in the replay record.
SIL / Offline-Replay Boundary
Returns a bounded scenario-level evidence decision while HIL, road testing, production deployment, live vehicle control and functional-safety certification remain outside scope.
Prove the Gate Before Broader Integration
Back-test the multi-sensor evidence gate against frozen historical replay sets with known validation-team disposition.
Freeze the accepted sensor fields, timestamp/freshness rules, localization context, missing-sector criteria, degradation context and customer-defined review thresholds before revealing the historical disposition. Broader integration is justified only if the same contract reproducibly surfaces material cross-sensor contradictions before physical escalation. This is a proposed validation methodology, not proof of road safety or production certification.
What you provide
Redacted offline replay evidence from the agreed radar, camera, LiDAR and localization sources, plus scenario identifiers and customer-defined review thresholds.
What BivectorAI actually hands you
Replay-level evidence decision
A bounded advance / review / block disposition for each reviewed replay under the agreed customer-defined acceptance boundary.
Cross-sensor consistency findings
A concise record of radar, camera, LiDAR and localization agreement conditions that support or weaken the scenario-level decision.
Missing / stale / contradictory evidence flags
An explicit list of incomplete sectors, stale timestamps, localization issues or cross-sensor contradictions requiring review.
Replayable input-linked review record
A retained record tying the evidence decision and review flags to the exact supplied offline replay package before physical escalation.
What we will never ask you for
Multi-sensor evidence review is designed to stay inside a bounded offline replay package, without widening into vehicle control or source-level access.
- Live vehicle access or vehicle-control authority.
- Production vehicle credentials.
- Your autonomy source code or controller implementation.
- Raw proprietary sensor data when a redacted replay package is sufficient.
How it actually runs
Start with a bounded evaluation, not a large commitment
Fixed scope / quoted per engagement. We define the evidence boundary, inputs, outputs and acceptance criteria before work begins.
We do not open with an annual license. If the bounded multi-sensor replay review does not establish useful decision evidence on the agreed customer-controlled problem, there is no reason to move into a broader commercial commitment.
Environment mapping, bounded data contract, evidence integration and agreed delivery scope.
Annual commercial access within the agreed product, program and deployment boundary.
Fixed program or campaign scope where a bounded evidence engagement is preferred.
Current evidence boundary: software-in-the-loop and offline replay evidence only. BivectorAI does not control a live vehicle. HIL validation, road validation, production deployment and functional-safety certification are not claimed.