Off-Road & Industrial Autonomy Evidence Assurance
Review autonomy replay evidence for terrain, stability, worker proximity, mission authorization and vehicle operating boundaries before physical trials.
Who this is for
- Mining, agriculture, yard, port and industrial-autonomy validation leads.
- Special-purpose vehicle integration engineers.
- Worker-safety and autonomous-operations reviewers preparing evidence for controlled field trials.
The problem you already have
Terrain slope and stability can invalidate a replay before field testing begins
Part 46 includes an off-road and industrial special-purpose vehicle embodiment where terrain slope is treated as a distinct safety context rather than folded into one aggregate autonomy score. For this product, that supports bounded replay review of whether the supplied terrain, stability and clearance evidence is coherent enough for human engineering disposition.
Unstructured environments remove the lane cues that make ordinary road replay easier to interpret
Mining, agriculture, yard, port and industrial scenarios can include dynamic obstacles, poor visibility, uneven surfaces and changing work zones without conventional lane structure. The review therefore keeps obstacle, visibility, multisensor and mission-zone evidence tied to the exact replay instead of inferring road-like assumptions.
Payload, tool and worker-proximity constraints must stay visible before physical escalation
An industrial replay can look operationally plausible while a payload attachment, tool state, worker boundary or remote-assistance condition remains unresolved. These conditions need an explicit review disposition, while live vehicle control, field authority and certification remain outside BivectorAI.
Real-World Operational Scenario: The Yard Replay That Passed Until the Loaded Vehicle Reached the Cross-Slope
Scenario: An autonomous industrial vehicle completes the nominal replay, but the loaded configuration encounters a cross-slope and worker-zone combination that was not represented in the summary KPI.
The validation team needs to decide whether that replay can advance toward a controlled field trial or should remain slowdown-required or blocked for engineering review.
WITHOUT this gate: successful path completion can hide the fact that terrain, stability, payload/tool and worker-boundary evidence no longer support the same operating interpretation.
WITH this gate: the supplied terrain, stability, mission-zone, worker-safety, payload/tool, multisensor and remote-assistance evidence is reviewed under one bounded offline contract. The result remains an evidence disposition; field operations and physical control stay with the customer.
What BivectorAI Provides
Terrain / Slope / Stability Evidence Review
Reviews whether supplied terrain, grade, stability and clearance context is coherent enough for the agreed replay decision without claiming physical vehicle control.
Unstructured-Environment & Visibility Review
Uses Part 46 technical lineage to surface obstacle, visibility and multisensor evidence gaps that matter when conventional lane structure or road assumptions do not apply.
Payload / Tool / Worker-Boundary Review
Keeps attachment state, payload context, worker proximity and mission-zone restrictions explicit so local operating constraints are not averaged away.
Offline-Only Field-Escalation Boundary
Returns review / slowdown-required / block findings while live industrial-vehicle control, HIL/field validation, production deployment and regulatory or functional-safety certification remain outside scope.
Prove the Gate Before Broader Integration
Back-test the off-road evidence gate against frozen historical replay cases with known engineering disposition.
Freeze the accepted terrain/stability fields, mission-zone and worker-boundary context, payload/tool state, multisensor evidence, remote-assistance conditions and review/slowdown/block criteria before revealing the historical disposition. Broader integration is justified only if the same contract reproducibly surfaces material operating-boundary conflicts before field escalation. This is a proposed validation methodology, not proof of field safety or production certification.
What you provide
Redacted offline replay evidence containing the agreed terrain, stability, mission-zone, worker-safety, payload/tool, multisensor and remote-assistance context.
What BivectorAI actually hands you
Replay-level mission evidence disposition
A bounded review / slowdown-required / block disposition for each supplied replay under the agreed operating boundary.
Terrain / traction / stability findings
A concise record of supplied terrain, traction, stability, clearance and worker-boundary conditions that support or weaken advancement.
Mission / payload / remote-assistance flags
An explicit list of payload/tool, mission-zone, worker-safety or remote-assistance conditions requiring customer engineering review.
Replayable input-linked review record
A retained record tying the disposition and boundary findings to the exact supplied offline replay package before physical testing.
What we will never ask you for
Off-road autonomy review begins with bounded offline replay evidence, not with access to a live industrial or special-purpose vehicle.
- Live industrial-vehicle control.
- Vehicle command or actuator credentials.
- Production fleet credentials.
- Unnecessary raw site data when redacted replay evidence 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 off-road 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 only. BivectorAI does not control a live industrial or special-purpose vehicle. HIL validation, field validation, production deployment and regulatory or functional-safety certification are not claimed.