Robotaxi & Autonomous Shuttle Evidence Assurance
Review driverless-operation replay evidence across vehicle, passenger, geofence and remote-assistance boundaries before physical escalation.
Who this is for
- Robotaxi and autonomous-shuttle validation leads.
- Driverless-platform safety and release engineers.
- Remote-assistance and fleet-operations leads responsible for evidence before road or fleet trials.
The problem you already have
A technically plausible drive can still violate the operating-design boundary
Part 46 separates L4 path evidence from operational-design-domain context. For this product, that supports a bounded replay review of whether the supplied route, geofence, weather/speed, multisensor and driverless-operating evidence still belong to the exact scenario being considered for advancement.
Passenger and minimum-risk evidence can change the disposition even when the path looks good
A robotaxi or shuttle replay can appear nominal until passenger-state, cabin-safety, minimum-risk or remote-assistance evidence is considered. Those conditions should remain explicit instead of being absorbed into a broad autonomy score.
Offline driverless assurance must remain separate from remote actuation and fleet authority
The current evidence boundary is software-in-the-loop and offline replay only. BivectorAI does not control a live robotaxi or shuttle, perform remote vehicle actuation, request production fleet credentials or claim HIL/road validation, deployment, regulatory certification or functional-safety certification.
Real-World Operational Scenario: The Robotaxi Replay That Passed the Route but Failed the Geofence-and-Passenger-State Review
Scenario: A driverless replay completes the planned urban path, but one segment leaves the declared operating boundary while the passenger-state evidence also requires a different minimum-risk or remote-assistance disposition.
The validation team needs to decide whether the replay can advance toward road or fleet testing or should remain review-blocked.
WITHOUT this gate: route completion can dominate the decision while geofence, passenger-cabin, minimum-risk or remote-assistance conditions remain buried in separate logs.
WITH this gate: the supplied driverless scenario, multisensor context, ODD/geofence boundary, passenger state, minimum-risk evidence and remote-assistance context are reviewed together. The result stays a replay evidence disposition; live actuation and release authority remain with the customer.
What BivectorAI Provides
ODD / Geofence Evidence Review
Reviews whether the supplied route and operating context remain inside the declared driverless boundary for the exact replay under consideration.
Passenger-State & Cabin-Safety Context Review
Keeps passenger-state and cabin-safety evidence explicit where it can alter the engineering disposition without requiring passenger identity data.
Minimum-Risk / Remote-Assistance Evidence Review
Uses Part 46 technical lineage to surface minimum-risk and remote-assistance conditions that require review before physical escalation, without issuing remote vehicle commands.
Offline-Only Driverless Boundary
Returns bounded replay findings while live robotaxi/shuttle control, remote actuation, HIL/road validation, deployment and certification remain outside scope.
Prove the Gate Before Broader Integration
Back-test the driverless replay gate against frozen historical robotaxi/shuttle cases with known validation-team disposition.
Freeze the accepted ODD/geofence fields, passenger-state context, minimum-risk conditions, remote-assistance evidence and review/block criteria before revealing the historical disposition. Broader integration is justified only if the same contract reproducibly surfaces material operating-boundary conflicts before physical escalation. This is a proposed validation methodology, not proof of road safety, type approval or production certification.
What you provide
Redacted offline replay evidence covering the agreed driverless scenario, multisensor context, operational boundary, passenger state and remote-assistance context.
What BivectorAI actually hands you
Replay-level evidence disposition
A bounded review / block disposition for each supplied driverless replay under the agreed operating and acceptance boundary.
Authority / geofence / passenger-state flags
An explicit list of operating-boundary, passenger-cabin or authority-context concerns that support or weaken advancement.
Minimum-risk / remote-assistance review record
A concise account of supplied minimum-risk, urban-interaction and remote-assistance evidence requiring engineering review.
Replayable input-linked evidence package
A retained record tying the disposition and review flags to the exact supplied offline replay package for customer engineering review.
What we will never ask you for
Robotaxi and shuttle evidence review is designed around bounded offline replay, without expanding into live actuation or unnecessary passenger-data access.
- Live robotaxi or shuttle control.
- Remote vehicle actuation authority.
- Production fleet credentials.
- Passenger identity data unnecessary for the agreed review.
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 robotaxi or shuttle 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 robotaxi or shuttle and does not perform remote vehicle actuation. HIL or road validation, production deployment, regulatory certification and functional-safety certification are not claimed.