Camera-Centric ADAS Evidence Assurance
Independent evidence assurance for camera-centric ADAS simulation and replay. It helps validation teams decide whether a replay package is coherent enough to advance, hold for review, or block before the next release gate — without taking control of a vehicle.
Who this is for
ADAS validation leads and test engineers
People reviewing camera-centric simulation and replay evidence before release or validation gates.
Perception and safety reviewers
Engineers responsible for surfacing occlusion, degraded evidence, stale inputs and uncertainty before a candidate is promoted.
Release and acceptance managers
Decision owners who need a compact evidence record before deciding whether a replay package advances, holds for review or blocks.
The problem you already have
Manual replay review consumes the engineers you need for validation
Camera failures, occlusion, stale evidence and inconsistent tracks can force engineers to revisit large replay sets by hand. That slows the release decision because the evidence has to be reconciled before anyone can trust promotion. The cost shows up as review time and another validation cycle when weak evidence is discovered late.
A good aggregate KPI can still hide the scenario that should stop promotion
A replay package can look acceptable at summary level while a small set of scenarios still contains evidence that should hold or block advancement. Reviewers then have to trace the local failure back to the decision manually. A bounded replay record makes the scenario-level evidence visible before the next gate.
You should not need a live vehicle or your source code for an evidence check
A useful review can start from a redacted simulation or replay package. It does not require live vehicle-control access, production driving credentials or source code for the perception or planning stack. That keeps the engagement at the evidence layer while your team retains control of what advances.
Real-World Operational Scenario: The Replay Set That Passed the KPI but Failed One Camera-Coverage Case
Scenario: A camera-centric ADAS candidate shows acceptable aggregate replay metrics, but a small night or occlusion subset contains stale, incomplete or internally inconsistent visual evidence.
The validation team needs to decide whether the package should advance, hold for review or block before the next release gate.
WITHOUT this gate: the aggregate KPI can dominate the decision while the local evidence failure is buried inside a large replay set and discovered only after another validation cycle.
WITH this gate: the bounded replay package is reviewed at scenario level for camera evidence quality, depth/lane/drivable-area coherence, stale or degraded inputs and review blockers. The result is a retained evidence decision; the customer still owns every release and vehicle decision.
What BivectorAI Provides
Camera-Evidence Coherence Review
Uses Part 46 technical lineage to review supplied camera-centric evidence for internally coherent visual confidence, depth, lane and drivable-area context without claiming control of the vehicle.
Occlusion / Degradation / Stale-Evidence Screening
Surfaces replay cases where missing coverage, degraded conditions, stale inputs or inconsistent perception evidence should remain review-required instead of disappearing inside an aggregate score.
Scenario-Level Replay Gate
Produces an advance / review / block evidence decision for the agreed replay scope, tied to the exact scenarios and supplied evidence that support it.
Simulation-Only Vehicle Boundary
Keeps live vehicle connection, actuation, production driving credentials and release authority outside the engagement while preserving a replayable validation record.
Prove the Gate Before Broader Integration
Back-test the replay gate against frozen historical ADAS scenario sets with known validation-team disposition.
Freeze the accepted replay fields, camera-evidence quality rules, occlusion/degradation conditions, stale-input criteria and advance/review/block acceptance rules before revealing the historical disposition. Broader integration is justified only if the same contract reproducibly surfaces material scenario-level evidence failures. This is a proposed validation methodology, not proof of production safety certification or live-vehicle performance.
What you provide
A redacted simulation or replay evidence package containing only the operational summaries needed for the agreed review. No live vehicle connection is required.
What BivectorAI actually hands you
A bounded decision gate
A clear advance / review / block decision tied to the exact replay package and agreed validation question.
Evidence-quality findings
A concise account of camera coverage, degradation, stale-input, coherence or other supplied-evidence conditions that weakened or supported the decision.
Replayable review record
A structured input-linked record your engineering or acceptance team can retain and compare across evaluation runs.
Human-owned next action
The record identifies what can advance and what remains review-required; BivectorAI does not execute vehicle actions or release the vehicle.
What we will never ask you for
Camera-centric validation can be reviewed from bounded simulation or replay evidence. The engagement is designed to avoid expanding into live-vehicle access, production credentials or unnecessary proprietary disclosure.
- live vehicle-control access or command authority;
- production driving credentials;
- source code for your perception or planning stack;
- unnecessary raw proprietary data when redacted replay evidence is sufficient.
How it actually runs
Strategic commercial terms
Strategic technology — direct commercial agreement only. No automated checkout applies to this line.
We do not open with an annual license. The first engagement is a fixed-scope evaluation. If it does not add useful decision evidence over your current review process, there is no reason to move into a broader commercial commitment; you keep the evaluation record and stop there.
Simulation / replay pilot
Fixed scope
Quoted per engagement, with no ongoing commitment required for the initial evaluation.
One bounded replay package and one review question, with no ongoing commitment.
Private integration / NRE
On request
Environment setup, evidence mapping, acceptance definition and controlled engagement scope.
Annual technology license
On request
Ongoing access within the agreed field, program and deployment boundary.
Program / campaign
On request
A bounded program where the assessment produces substantial exportable decision evidence.
Tier: S-TECHNICAL. Strategic technical / scientific program.
Exclusive field-of-use rights, sublicensing, source-level technical data, IP assignment, unusually broad bulk usage and dedicated infrastructure are negotiated separately.
Scope the S21 engagement
Request S21 Strategic Discussion →Email fallback: contact@bivectorai.com
Current boundary: S21 operates on simulation and replay evidence only. It does not connect to or control a live vehicle.
Protected implementation details remain outside the public commercial description.