OEM Adoption

Deterministic Safety + Autonomy stack for OEM adoption

FieldSpace is a deterministic end-to-end autonomy stack, engineered for safety cases: explicit failure semantics, reproducible tests, and audit-ready traces. OEMs can adopt it as the primary stack or stage rollout via observer/shadow mode to de-risk integration.

Integrates after your fusion layer (object list / tracks). No new sensor pipeline required, no retraining, and no change to control authority.

Built for adoption: contracts, determinism, validation

OEM teams need components that are measurable, auditable, and safe to integrate. FieldSpace is engineered around deterministic behavior, explicit failure modes, and repeatable test suites.

💰

Compute + latency budget

Designed to live inside your existing compute envelope. Deterministic, low-latency processing suitable for real-time constraints.

  • • CPU-friendly runtime path
  • • Explicit timing targets and budgets
  • • No new sensor pipeline required
🏁

Integration patterns

Drop-in as a safety observer: subscribe to fused objects, publish hazards/cost maps/alerts. Works in log replay and on-vehicle parallel runs.

  • • ROS 2 topics or service integration
  • • Log replay and regression pipelines
  • • Planner-ready hazard constraints
🛡️

Auditability + explainability

Every alert is traceable to structured triggers and bounded rules. Outputs are easy to log, replay, and review in safety cases.

  • • Structured hazard output
  • • Replayable metrics
  • • Documented interface contract
🔒

Safety-ready contracts

Defined states (NORMAL/DEGRADED/FAILED), timing guarantees, determinism guarantees, and conservative behavior under degradation.

  • • Transparent safety metrics
  • • Explainable decisions
  • • Safety certification support
📊

Validation suites

Deterministic scenario suites + regression gates: lead time, FP/FN proxies, stability, latency, and memory growth over long runs.

  • • Synthetic scenario validation
  • • Measured latency benchmarks
  • • Lead time improvements
🎯

Complements neural perception

Neural perception produces tracks; FieldSpace produces deterministic safety signals and constraints to reduce planner surprises and improve auditability.

  • • Consistent behavior under distribution shift
  • • Clear failure modes and caps under degradation
  • • Designed for replay + safety-case workflows

Why OEMs add deterministic layers

End-to-end neural systems can be powerful, but they are difficult to certify because they lack stable failure semantics. FieldSpace provides a deterministic safety contract around your existing stack.

End-to-end neural (typical pain points)

  • Non-determinism: same input may yield different outputs due to stochastic components and drift.
  • Unclear failure modes: difficult to bound behavior under distribution shift.
  • Audit burden: hard to produce compact, human-readable “why” traces for safety cases.
  • Regression risk: retraining can move behavior unexpectedly across scenarios.

FieldSpace methodology (engineering guarantees)

  • Deterministic: identical inputs produce bit-identical hazard outputs.
  • Contracted states: NORMAL / DEGRADED / FAILED with conservative caps under degradation.
  • Planner-ready outputs: hazards + TTC + costmaps + explainability triggers.
  • Validation-first: scenario suite + latency and stability gates to catch regressions early.
Integration Architecture

How OEMs deploy FieldSpace

Deploy FieldSpace as your primary deterministic autonomy stack, or stage adoption by tapping after fusion as a safety observer to de-risk integration. In both cases, outputs are validated, replayable, and audit-ready.

Staged rollout (observer mode)

Your Sensors
Cameras, LiDAR, Radar
Your Stack
Perception NN → Fusion
FieldSpace
Safety Observer
Your Stack
Planner / MPC
Vehicle
Control

Inputs We Accept

Fused Object Tracks (Preferred)

Object lists you already publish from your perception fusion. This is the preferred path for AV OEMs—no new data pipelines needed.

Camera Frames (Optional)

Low-rate camera stream dedicated to safety analysis if you prefer direct sensor input.

LiDAR / Radar Objects

These show up through your fused-object API. We consume them as part of your existing fusion output.

Data Volume Story

We do not ask you to mirror terabits per second of raw sensor data. For Aurora-like stacks, we consume:

  • Object list / tracks you already publish (KB/s, not TB/s)
  • Or a low-rate camera stream dedicated to safety analysis
  • Our PDE field runs on a compressed grid representation (256×64)

Bottom line: "I have terabits per second coming off 13 cameras plus multiple LiDARs; I can't just duplicate that" — you don't have to. We tap your existing fusion output.

Compute Budget

FieldSpace is designed to live inside your existing compute envelope, not demand a new ECU.

0.20ms
PDE step avg (Numba)
~1.7ms
End-to-end hazard update
<4ms
p99.9 @ 40 Hz
<1%
CPU budget @ 40 Hz

Reference hardware: Intel i7 / NVIDIA Orin / equivalent ARM

OEM evaluation playbook

A practical, engineering-driven evaluation that plugs into your existing replay and safety workflows.

Phase 1
Interface wiring
Subscribe to fused objects + ego state; publish hazards + status + optional costmap.
Phase 2
Log replay
Run deterministically on historical logs; generate traces, metrics, and diffs.
Phase 3
Safety suite
Execute standard scenarios + OEM-defined edge cases; quantify lead time and stability.
Phase 4
Parallel on-vehicle
Shadow mode beside your planner; verify latency and failure mode behavior.

Suggested KPIs

Safety: time-to-alert, TTC quality, FP/hr, monotonic escalation, degraded caps.
Performance: end-to-end latency, p99, CPU budget, memory growth over 1k+ frames.
Integration: time-to-first-result, surfaces touched, replay compatibility, trace auditability.

The Long-Tail Safety Challenge

Autonomy teams know that the hardest scenarios—debris, occlusions, sliding cargo—account for a small percentage of miles but a large share of safety risk. FieldSpace is designed to help address these cases with physics-based hazard reasoning.

~5%

Long-Tail Scenarios

A small fraction of driving scenarios accounts for a disproportionate share of perception challenges and safety risk

+0.62s

Mean Lead Time

Internal Safety Suite shows earlier hazard alerts compared to baseline in synthetic scenarios

<4ms

p99.9 Latency

Safety Observer pipeline measured at under 4ms for 99.9% of frames at 40 Hz on reference hardware

FieldSpace Safety Observer

Deploy as a safety observer beside perception, or run the full deterministic stack as an engineering harness for end‑to‑end validation. Produces structured hazards, cost maps, and safety envelopes for your planner.

4 Weeks
Pilot Program
~1.7ms
Avg Latency
Pilot Ready
Safety Suite Validated

Seamless Integration Process

Our proven integration methodology gets your vehicles to market quickly with minimal disruption to your existing development processes.

1

Discovery & Planning

Deep dive into your vehicle architecture, requirements, and integration goals. Define technical specifications and timeline.

Month 1
2

Hardware Assessment

Evaluate existing camera systems and compute units. Recommend minimal hardware upgrades if needed.

Month 2
3

Software Integration

Integrate FieldSpace ADS with your vehicle's CAN bus, sensors, and control systems. Custom API development.

Months 3-4
4

Testing & Validation

Comprehensive testing program including closed-course, public road, and edge case validation. Safety certification support.

Months 5-6

Integration Support Package

Technical Support

  • Dedicated integration engineer
  • 24/7 technical hotline
  • Comprehensive API documentation
  • Sample code and SDKs

Business Support

  • Regulatory compliance guidance
  • Marketing and launch support
  • Joint PR and co-marketing
  • Training for your team

White-Label Solutions

Deliver FieldSpace technology under your brand with fully customizable interfaces, features, and user experiences.

Custom User Interface

Design the autonomous driving interface to match your brand guidelines and user experience standards.

  • • Brand-consistent design language
  • • Custom icons and animations
  • • Configurable dashboard layouts
  • • Voice command integration

Feature Configuration

Choose which autonomous driving features to enable based on your market positioning and regulatory requirements.

  • • Selectable autonomy levels
  • • Custom safety profiles
  • • Regional compliance settings
  • • Performance tuning options

Data Ownership

Maintain full control over your customer data and vehicle telemetry with flexible data sharing agreements.

  • • Customer data remains yours
  • • Configurable telemetry sharing
  • • Privacy-compliant by design
  • • Custom analytics dashboards

White-Label Benefits

$

Revenue Model

License FieldSpace technology as your own proprietary system

🎯

Market Position

Differentiate from competitors with superior autonomy

Time to Market

Launch autonomous vehicles in months, not years

🛡️

Risk Mitigation

Proven technology reduces development risks

Technical Specifications

FieldSpace ADS delivers industry-leading performance with minimal hardware requirements and maximum compatibility.

Internal Benchmarks

Safety Observer Avg Latency~1.7ms
p99 Latency @ 40Hz<3ms
p99.9 Latency @ 40Hz<4ms
Mean Lead Time (synthetic)+0.62s
Prediction Horizon0.5–2.0s

Note: Internal benchmarks on reference hardware. Pilot KPIs measured on your platform.

Integration Requirements

Input FormatFused Objects / Detections
Compute ClassCPU-class hardware
ROS 2 SupportYes
Grid Resolution256×64
OutputHazardObjects, Cost Maps

API Integration Points

Vehicle Control

Steering, acceleration, braking integration via CAN bus

Sensor Fusion

Camera feeds, GPS, IMU data processing

HMI Interface

Dashboard display, voice commands, mobile app

Ready to evaluate a deterministic full stack?

Run the full deterministic stack on your logs and scenarios and produce replayable metrics + traces for safety review. If you prefer staged adoption, we can start in observer/shadow mode and expand scope after validation.