Same input. Same output. Every time.
FieldSpace is an end-to-end autonomy stack — perception through control — built on physics equations, not neural networks. Fully certifiable. Fully explainable. Built for OEM adoption.
Autonomy fails where humans need it most.
Today's self-driving systems rely on neural networks to make decisions. This reliance creates fundamental reliability issues that block trust, scale, and certification.
Phantom Braking
Sudden braking with no clear reason. Reacts to sensor noise and shadows, endangering passengers and trailing vehicles.
Hesitation at Merges
Indecisive behavior at intersections and highway merges. The system flip-flops between go and stop, creating dangerous situations.
Weather Failures
Inconsistent behavior in rain, glare, snow, or sensor dropouts. Performance degrades when conditions change — exactly when safety matters most.
Remote Operators
Hidden dependence on human teleoperators. One person monitoring 30-60 vehicles cannot react faster than the technology should.
Neural networks are guessing, not deciding.
Neural networks predict outcomes based on probability. They change decisions from moment to moment based on noise, not logic. This creates the Black Box Problem:
Hard to explain
We don't know why it braked. The decision trace is opaque.
Hard to reproduce
It might not brake next time. Same scene, different output.
Hard to certify
Regulators cannot approve variance. Certification requires determinism.
Physics equations, not neural networks.
FieldSpace models traffic as a continuous field governed by partial differential equations — continuity, velocity, and potential. The same math that predicts fluid dynamics now predicts vehicle behavior. Deterministic by construction.
Density + Velocity + Potential
0.2ms solve time (Numba)
4,777 FPS throughput
Traffic Field Solver
Solves continuity, velocity, and potential PDEs on a 2D grid to model traffic flow. Seeds density from detected objects and evolves fields to predict compression and hazards — using physics, not pattern matching.
Temporal Coherence
Non-Markovian intent persistence layer. Evidence accumulates over time with hysteresis — the system only switches behavior when evidence stays strong. Emergency override bypasses coherence when TTC < 1.5s. This is what eliminates flip-flopping.
Safety Observer
Standalone safety monitoring with zero control authority. Accepts fused objects from any OEM perception stack, runs PDE analysis, and outputs structured hazard objects with full explainability traces. Monotonic alert escalation — no flickering.
Planning & Control
Full autonomous pipeline: behavior planning, motion planning with fixed-iteration optimization, pure pursuit steering, PID speed control. Deterministic by construction — quantized outputs for replay stability. Safety supervisor with E-stop and rate limiting.
Measured, not claimed.
Five safety-critical scenarios. CARLA simulation with synthetic ground truth. Every scenario shows earlier hazard detection, zero false positives, and all braking margins met.
| Scenario | Baseline Lead | FieldSpace Lead | Gain |
|---|---|---|---|
| Falling Debris | -0.50s | +0.20s | +0.70s |
| Sudden Cut-In | -0.10s | +0.20s | +0.30s |
| Occluded Pedestrian | -0.30s | +0.20s | +0.50s |
| Stopped Vehicle | -0.40s | +0.20s | +0.60s |
| Sliding Cargo | -0.80s | +0.20s | +1.00s |
Every OEM needs this decision layer.
A unified deterministic platform for diverse autonomy needs. One core technology — deployed across every vertical.
Passenger Vehicles
ADAS through full autonomy. Eliminate phantom braking and build passenger trust.
Commercial Fleets
Mid-mile delivery, long-haul trucking. Deterministic behavior on repeated routes.
Industrial & Agriculture
Farming equipment, warehouse robots, mining vehicles interacting with human workers.
Defense & Robotics
Autonomous ground vehicles and robots operating alongside personnel in complex environments.
Software that scales with production.
Revenue grows as OEMs move from evaluation to production. A capital-efficient model built for long-term partnerships.
Pilot Fees
4-week evaluation with log replay, ROS 2 integration, and Safety Suite validation on your data.
Integration & Support
On-vehicle parallel runs, per-model agreements, and dedicated engineering support.
Platform License
Full-stack deployment across vehicle lines. OEM-specific tuning and validation suites.
Volume Production
Per-vehicle royalty. Revenue scales linearly with OEM production volume.
Two ways to deploy.
FieldSpace integrates with your existing stack via REST + WebSocket API or ROS 2. Choose safety monitoring alongside your perception, or run the full deterministic pipeline.
Safety Observer
Run FieldSpace in parallel with your existing autonomy stack. Zero control authority — it monitors, detects hazards, and outputs structured alerts with explainability traces. No changes to your planner.
Full Deterministic Stack
Run the complete FieldSpace pipeline: PDE field solver, temporal coherence, behavior planning, motion planning, and vehicle control. End-to-end deterministic — perception through actuation.
The industry has hit a wall.
We have the ladder.
Neural autonomy is stalling. Remote operators are a liability. Regulators demand explainability. FieldSpace is the deterministic decision layer every OEM needs to move forward.
Not smarter. Not flashier. Just predictable, explainable, and safe.