Physics Engine
Physics-first hazard and safety engine that runs beside your perception stack. FieldSpace consumes your detections and fused objects, then builds a live physics field to track motion, predict short-horizon risk, and publish HazardObjects and cost maps.
Engine Architecture
High-performance modular system designed for real-time autonomous vehicle deployment
Real-Time Core
Sub-10ms processing pipeline with deterministic execution guarantees for safety-critical operations.
- • Lock-free concurrent processing
- • Memory pool allocation
- • SIMD optimization
- • Zero-copy data flow
Compute Engine
GPU-accelerated physics calculations with adaptive precision for optimal performance-accuracy balance.
- • CUDA kernel optimization
- • Multi-precision arithmetic
- • Dynamic load balancing
- • Hardware abstraction layer
Data Pipeline
High-throughput sensor data processing with automatic quality assessment and error correction.
- • Multi-camera synchronization
- • Automatic calibration
- • Noise reduction algorithms
- • Data integrity validation
Beyond Machine Learning
Traditional autonomous systems rely on pattern matching from training data. FieldSpace uses fundamental physics to understand reality as it actually is.
Continuous Physics Modeling
Objects modeled as continuous fields using PDE-based algorithms
Deterministic Inferences
Same inputs produce the same HazardObjects and cost maps
Long-Tail Scenario Focus
Designed to improve robustness in debris-like and occluded scenarios
Technical Specifications
Production-ready performance metrics and system requirements
What FieldSpace Sees (and Doesn't See)
Your existing perception stack is still responsible for semantics and rules of the road. FieldSpace's world model cares about "who is moving where and how fast" and flags potential collisions.
✓ What We Model
- ●Dynamic Hazards
Debris, vehicles, pedestrians, bikes, cargo shedding, occluded motion
- ●Motion as a 2D Grid Over Time
Objects with velocities on a 256×64 grid, updated at 40 Hz
- ●HazardObjects Derived from Fields
Structured output: risk scores, TTC, position, velocity, cones
- ●Short-Horizon Prediction
0.5–2.0 second forward projection of motion fields
✗ What We Don't Model
- ●Semantic Signs & Symbols
Stop signs, lane arrows, traffic lights, road markings
- ●Rich Agent Intent
Lane-keeping policies, courtesy lane changes, turn signals
- ●Long-Horizon Behavior Prediction
We don't predict what a driver will do 10 seconds from now
- ●Object Classification
We don't classify "this is a car" vs "this is a truck"—your stack does
Event-Style Reasoning on Standard Sensors
We do not require event cameras. Event detection is done in software.
How It Works
- 1.We simulate an "event camera" in software over regular camera / fused inputs
- 2.We treat any significant change in the scene as an "event"
- 3.We only allocate heavy reasoning to regions with events
- 4.That's how the PDE solver sits at ~1.7 ms avg latency
Key Points
- ✓No event camera required—works with standard cameras or fused objects
- ✓Compute reduction—focus processing where motion matters
- ✓Deterministic—same inputs produce same outputs
- ✓Low latency—sub-4ms p99.9 at 40 Hz
Add a Physics-Based Safety Layer
Ready to integrate a deterministic safety observer into your autonomous stack?