BivectorAI provides reusable, core-bound assurance for steering, braking, acceleration and trajectory decisions across five major autonomous-vehicle architecture groups. The system evaluates whether a proposed action remains supported by sensor, localization, operating-domain, vehicle-state and physical evidence before the next engineering phase.
BivectorAI cung cấp lớp assurance có thể tái sử dụng cho quyết định lái, phanh, tăng tốc và quỹ đạo trên năm nhóm kiến trúc xe tự hành lớn. Hệ thống kiểm tra hành động đề xuất có còn đủ căn cứ từ cảm biến, định vị, phạm vi vận hành, trạng thái phương tiện và giới hạn vật lý trước khi được chuyển sang giai đoạn kỹ thuật tiếp theo hay không.
The current public-safe automotive portfolio has completed an engineering closeout across five reusable architecture groups. Each group has executable tests, tracked evidence artifacts, deterministic replay and fail-closed core-removal evidence.
Portfolio ô tô hiện tại đã hoàn tất engineering closeout cho năm nhóm kiến trúc có thể tái sử dụng. Mỗi nhóm đều có kiểm thử thực thi, artifact được theo dõi, replay xác định và bằng chứng fail-closed khi chuỗi core bắt buộc không khả dụng.
Reusable automotive architecture groups.
Executable architecture-specific scenarios passed.
Minimum tracked runtime, test and closeout artifacts.
No hard failures in the canonical portfolio audit.
Conventional validation often concentrates on perception metrics, scenario completion or controller output. BivectorAI focuses on the promotion boundary where software is expected to become motion: whether the proposed vehicle action remains supported, bounded, reproducible and reviewable.
Các quy trình validation thông thường thường tập trung vào chỉ số perception, số lượng scenario hoặc output của controller. BivectorAI tập trung vào ranh giới promotion nơi phần mềm chuẩn bị trở thành chuyển động: hành động đề xuất có đủ bằng chứng, còn trong giới hạn, có thể tái lập và có thể review hay không.
Evaluate the proposed steering, braking, acceleration or trajectory action—not only the confidence of the model that produced it.
Produce readable evidence gaps, operating limits and reasons for normal assurance, review, slowdown, minimum-risk response, blocking or emergency handling.
Missing required evidence, invalid boundaries or unavailable core execution cannot silently produce a normal approved result.
Each architecture group has its own evidence model, operational disposition logic and scenario matrix while remaining bound to the shared BivectorAI autonomous-driving product core and protected automotive physical-action kernel.
Mỗi nhóm kiến trúc có mô hình bằng chứng, logic disposition và scenario matrix riêng, nhưng đều được ràng buộc với product core xe tự hành dùng chung và automotive physical-action kernel được bảo vệ của BivectorAI.
Multi-camera role coverage, frame freshness and synchronization, camera health, calibration and provenance, visibility, occlusion, lane topology, drivable path, object tracking, vulnerable-road-user coverage, depth evidence and localization consistency.
Required modality coverage, source freshness, sensor health, timestamp alignment, localization confidence, cross-sensor agreement, redundancy, degradation handling and fail-closed physical-action assurance.
Driverless operating authority, geofenced ODD, passenger and door context, route validity, remote-assistance boundaries, minimum-risk capability, emergency handling and multisensor degradation.
Gross mass, payload and axle limits, load distribution, center-of-gravity boundary, mass-sensitive stopping distance, brake health, road grade, surface friction, trailer stability, cargo state, dispatch, route, bridge and service authorization.
Terrain class, longitudinal and cross slope, traction, sinkage, visibility, roll, pitch, rollover margin, terrain speed, ground clearance, mission authorization, worker and exclusion-zone protection, payload, tool state and minimum-risk capability.
Architecture-specific adapters normalize customer-authorized replay, logs, policies and vehicle limits. The reusable product layer then invokes the autonomous-driving product core and protected automotive physical-action kernel without exporting private technology.
Converts camera, multisensor, L4, logistics or industrial evidence into a customer-safe assurance request.
Evaluates operating-domain, evidence, authority and vehicle-action boundaries through a reusable product contract.
Supplies the BivectorAI deep-tech capability while keeping private internal algorithms outside the customer deliverable.
Returns checks, reasons, operational disposition, replay hash, boundary status and core-binding evidence.
A pilot can begin from customer-authorized SIL replay, planner outputs, sensor records, vehicle-state logs, operating policies and physical limits without requiring BivectorAI to control a live vehicle.
Architecture-specific normal, review, slowdown, minimum-risk, block or emergency dispositions for each replay.
Missing, stale, inconsistent, unauthorized or physically invalid evidence that prevents normal promotion.
Byte-identical repeat execution and customer-safe hashes for regression, model, planner, calibration and policy comparison.
Evidence that the reusable product core and protected kernel were invoked, with fail-closed behavior when the required path is absent.
Customer-safe outputs for ADAS, autonomy, validation, safety, management, partner and procurement review.
A clear record of what is ready for the next engineering step, what needs more evidence and what must remain blocked.
The canonical portfolio audit records 100 passing scenario tests, 35 tracked artifacts, valid registries and engineering closeouts across all five reusable architecture groups.
22 camera-centric, 13 multisensor, 15 L4, 19 logistics and 31 industrial scenarios.
Test audits, positive runtime outputs, deterministic replay evidence, core-removal audits, closeouts and SHA256 manifests.
Identical public-safe replay input produces identical customer-safe output and stable result hashes.
Removing the required core runtime causes the architecture product to fail closed without writing a normal output result.
Each product records customer scope, product family, reusable use cases, core dependency and non-generic product status.
Every reusable architecture product has a tracked, passing engineering closeout under the same public-safe claim boundary.
BivectorAI has moved beyond a single customer adapter. The current automotive portfolio contains reusable products for passenger ADAS, multisensor autonomy, robotaxis and shuttles, logistics fleets and industrial autonomous vehicles.
BivectorAI không còn chỉ có một adapter cho một khách hàng. Portfolio hiện tại gồm các sản phẩm có thể tái sử dụng cho passenger ADAS, multisensor autonomy, robotaxi và shuttle, logistics fleet và phương tiện tự hành công nghiệp.
BivectorAI does not compete by adding another perception model. It evaluates whether actions generated by existing autonomy stacks remain sufficiently supported for engineering promotion.
Customer-specific data and policies can be adapted at the edge without rebuilding the complete assurance product for every vehicle program.
Initial SIL evidence reviews can expand into recurring regression, model, planner, calibration, policy, fleet and safety-review workflows.
Five reusable architecture products, 100 passing scenarios, deterministic replay, tracked evidence artifacts, valid closeouts, required core invocation, fail-closed core removal, no private-core export and no vehicle control authority.
BivectorAI does not claim live vehicle actuation, HIL validation, road or field validation, production deployment, functional-safety certification or customer validation from this evidence suite.
Customers receive evidence summaries, checks, operational dispositions, reason codes, reproducible results, hashes and engineering recommendations. BivectorAI’s protected internal technology remains private.
Khách hàng nhận summary bằng chứng, các check, operational disposition, reason code, kết quả có thể tái lập, hash và khuyến nghị kỹ thuật. Công nghệ nội bộ được bảo vệ của BivectorAI vẫn giữ riêng tư.