LiDAR performance is a system problem — not a parts problem.
Science Counter is building a new LiDAR architecture — and the on-device Physical AI that turns its data into understanding — for safety-critical autonomy. A patent-protected, pre-commercial venture.
Modern autonomy needs robust 3D perception under sunlight, weather, and multi-sensor traffic — and perception a machine can reason about in real time. Our focus is whole-system architecture: optics, timing, signaling, perception, and validation designed together, so the sensor delivers a causal model of the scene the on-device AI can act on directly.
What we build
- Designed for high‑fidelity signaling and precise timing
- Built for robustness via redundancy (multiple looks)
- Integrates perception objectives into the sensing stack
Why now
- ToF ambiguity and timing budgets constrain acquisition
- Coexistence/interference become systemic in traffic
- Raw data rates stress compute and validation pipelines
Until sensing can be trusted, safety-critical autonomy stays stuck in pilots — and the sensor is what holds it back.
How we prove it
- Range / precision / stability under sunlight & weather
- Latency and revisit behavior within decision windows
- Repeatable benches + field datasets
Frame rate
Higher update rate reduces stale perception and improves control responsiveness.
Point density
Enough points on small objects matters more than peak “points/sec” marketing.
Robustness
Redundancy, revisits, and consistency checks within one decision window improve reliability.