Current Challenges in LiDAR Technology
Understanding the limitations and obstacles in modern LiDAR systems and the path towards next-generation solutions.
The operating principle hasn't changed since 1961
A year after the first laser, someone timed its echo to measure distance. Every LiDAR since has refined the packaging around that same idea.
Sixty years of new packaging on one principle — and the costs it builds in have never gone away.
Beam Steering in FMCW LiDAR
Beam steering is FMCW’s biggest engineering challenge. We’ve captured a draft breakdown of the three main approaches (MEMS mirror, OPA, wavelength+grating) and why seamless 360° remains hard.
Prior art & deep reads
Longer-form notes on architecture and market context, followed by filed language on non–beam-steered illumination and delay-line encoding.
Prior art & deep reads
Longer-form notes on architecture and market context, followed by filed language on non–beam-steered illumination and delay-line encoding.
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Environmental Interference
Performance degradation in adverse weather conditions like rain, snow, and fog
Range Limitations
Decreased effectiveness at longer distances and varying reflection surfaces
Cost Barriers
High manufacturing and implementation costs limiting widespread adoption
Processing Overhead
Computational challenges in real-time point cloud processing
Power Consumption
High energy requirements affecting system efficiency and battery life
Integration Complexity
Challenges in seamless integration with existing systems and infrastructure
How the approaches compare
Every mainstream LiDAR approach optimizes within the same 1961 principle. Here is how they trade off — and where a different architecture changes the table.
| ToF | FMCW | MEMS scan | OPA solid-state | Flash | This architecture | |
|---|---|---|---|---|---|---|
| Beam steering | Mechanical | External (MEMS/OPA) | Micromirror | Phase control | None (floods) | None required |
| Moving parts | Yes | Usually | Yes (micro) | None | None | None |
| Field of view | 360° (spinning) | Narrow, tiled | Narrow, tiled | Limited angle | Wide, short range | 360° simultaneous |
| Velocity | No | Yes (Doppler) | No | No | No | Yes |
| Cost vs resolution | Scales up | Scales up | Scales up | High, complex | Detector-heavy | Flat — decoupled |
| All-weather | Limited | Better | Limited | Limited | Short range | High |
| Output | Point cloud | Cloud + velocity | Point cloud | Point cloud | Point cloud | Causal comprehension |
| Maturity | Mature | Emerging | Deployed | Immature | Niche | In development |
Simplified comparison · general characteristics of each approach
Technical Analysis
Performance Metrics
Why Traditional LiDAR is not a viable solution for the future?
Total solution end to end not possible with traditional LiDAR, but possible with our solution.
Key Differences from Traditional LiDAR
- Much Simpler principle of operation
- Unprecedented Data Acquisition Rate
- No Moving Parts
- Novel Signaling Mechanism
- Optical Fiber + Continous Optical Illumination
- Novel scanning mechanisms
- Energy Efficiency
- Extremely High Data Acquisition Rate
- Environmental Resistance
- Physical-AI comprehension of the world
- Platform Agnostic
What a next-generation sensor must do
Fixing one limitation at a time isn't enough — they compound. A sensor built for autonomy has to clear all of these at once:
Clearing one or two is incremental. Clearing all of them takes a new architecture.
Future Solutions
Architectural sensing
System-level photonic + timing architecture for higher certainty, robustness, panoramic field-of-view capability, and scalable long-range
AI Integration
Advanced Trustable AI algorithms and sophistacaed data processing for sorround state understanding/comprehension
Platform Agnostic
Platform agnostic architecture for seamless integration with existing systems and infrastructure