Deterministic Full-Stack Autonomy
FieldSpace is a deterministic end-to-end autonomy stack (perception → prediction → planning → control), built around reproducible tests, explicit failure modes, and audit-ready traces. OEMs can deploy it as the primary stack or integrate it incrementally (observer/shadow) to de-risk adoption.
A Physics Layer That Makes Your Stack Safer
Your neural networks detect and classify. FieldSpace runs a parallel physics model that tracks motion, predicts short-horizon risk, and produces structured HazardObjects and cost maps for your planner.
Hardware Friendly
Works with your existing cameras and fused object streams. Runs in ~1.7ms on a 256×64 grid at 40 Hz using CPU-class hardware, leaving most compute for your main stack.
Fast Integration Path
Four-week pilot program with log replay, ROS 2 integration, and a Safety Suite that measures real KPIs on your data. On-vehicle parallel runs can start once topics are wired.
Explainable Safety Layer
Deterministic, physics-based outputs: HazardObjects, risk scores, time-to-collision, and cost maps that are straightforward to log, replay, and audit.
Long-Tail Focus
Designed as a co-processor for the 5% of scenarios that are hardest for traditional perception—debris, sliding cargo, or occluded motion.
Measured Performance
Internal Safety Suite shows +0.62s mean extra reaction time across synthetic scenarios. Latency under 4ms p99.9 at 40 Hz on reference hardware.
Regulator Friendly by Design
Built around transparent metrics, replayable logs, and a documented interface contract describing timing, failure modes, and system states.
Powered by NVIDIA Technology
FieldSpace is part of the NVIDIA Inception program, using NVIDIA GPUs to accelerate real-time physics-based hazard detection, cost map generation, and Safety Suite evaluation for autonomous driving research.
Lead the Autonomy Revolution
Deploy FieldSpace as a deterministic full-stack autonomy system, backed by validation suites and audit-ready traces. For staged rollout, you can also run observer/shadow mode beside your existing stack.
4-week pilot program. Designed for supervised deployment. Built for measurable, deterministic validation.