AREYLight High Efficiency Ai Engine
Autonomous Lighting Technology
Redefine Energytech & Conserve electricity usage by ensuring that lighting units can be controlled remotely and efficiently throught the use of artificial intelligence
Installation Sites
Preception Technology
Efficiency Rates
Save Rates
Learning Tech
Intelligence at the Edge.
Insights in the Cloud.
From radar pulses to deep reports — AREYLight’s AI Engine is your city’s silent genius.
Multi-Layer AI Processing
Our AI Engine processes environmental data through specialized neural networks, delivering intelligent lighting responses in milliseconds.
Sensor Fusion
Radar, PIR, Lux sensors
Computer Vision
YOLOv9 object detection
Cognitive Reasoning
Qwen 3-VL analysis
Live
Multi-Layered AI Architecture
From environmental sensing to intelligent response — our AI Engine processes data through specialized neural networks.
Sensor Layer
-
Radar movement detection -
PIR human presence -
Lux ambient light measurement -
Voltage monitoring
Vision Layer
-
YOLOv9 real-time detection -
Pedestrian identification -
Vehicle classification -
Animal recognition
Reasoning Layer
-
Qwen 3-VL multimodal analysis -
Risk assessment scoring -
Optimal light level calculation -
Contextual understanding
Insights Layer
-
DeepSeek R1 trend analysis -
Energy savings optimization -
Predictive maintenance -
Natural language reports
Real-Time Processing Pipeline
Our AI Engine processes data through each layer in milliseconds, enabling intelligent lighting responses while maintaining strict privacy standards.
~50ms
~120ms
~30ms
Real-World Scenario
See how our AI Engine responds to complex situations with intelligent lighting decisions.
It’s 3AM in Synevyr, snowy, -18°C
A truck loses traction on icy road. YOLOv9 detects skidding motion while Qwen 3-VL analyzes weather conditions and road surface.
RL agent boosts adjacent lights to 100% intensity, creating safe zone for recovery.
DeepSeek R1 generates incident report with timestamps, environmental data, and response metrics.
Gemma 3 explains the event in plain Ukrainian: “Світло увімкнено на 100% через небезпечне ковзання вантажівки.”
Smart Rural Roads
Adaptive lighting for country roads that responds to vehicles, pedestrians, and wildlife while conserving energy.
60-80% energy savings vs static lighting
Urban Dynamic Lighting
Intelligent light adjustment based on pedestrian density, weather conditions, and special events.
±6% lighting accuracy for optimal visibility
Predictive Maintenance
AI predicts failures before they occur, optimizing maintenance routes and reducing downtime.
0.91 ROC AUC for failure prediction
Benchmark Performance
Our AI Engine combines cutting-edge models for unparalleled smart lighting intelligence.
Model Specifications
| Function | Model | Performance |
|---|---|---|
| Object Detection | YOLOv9 | mAP50 ≈ 74% |
| Image Reasoning | Qwen 3-VL | Lighting accuracy ±6% |
| Language Q&A | Gemma 3 | Response < 0.8s |
| Reports & Forecast | DeepSeek R1 | Trend insights, budget optimization |
| Dim Decision | Q-learning + RL | 60–80% energy savings |
| Maintenance Prediction | XGBoost | ROC AUC 0.91 |
RURACTIVE Challenge Fit
| Criteria | Response |
|---|---|
| Road Safety | YOLO + Qwen detect and reason in real-time |
| Energy Saving | RL agent + DeepSeek optimizations |
| Community Trust | Natural language summaries via Gemma |
| Cost-Efficiency | Retrofit model, low OPEX |
| Maintenance | Predictive alerts + optimized field routing |
Edge Deployment Specs
Deep Integration
AREYLight AI connects sensor data with advanced AI models for comprehensive smart lighting intelligence.
Lux, motion, and voltage data streamed to DeepSeek R1 for historical analysis and pattern detection.
YOLOv9 object detection feeds into Qwen 3-VL for contextual understanding and risk assessment.
Gemma 3 enables operators to query the system: “Why did this streetlight turn on at 2:17 AM?”
Multi-Layered
AI Flow
From sensor input to intelligent response in milliseconds
Sensors
Radar, PIR, Lux
Trigger events (movement, darkness)
Vision
YOLOv9
Detect objects (pedestrians, vehicles)
Reasoning
Qwen 3-VL
Analyze scene, suggest light levels
Insights
DeepSeek R1
Generate reports, risk scores
Interaction
Gemma 3
Chatbot & dashboard assistant
Seamless AI Integration
The AREYLight AI Engine processes data across multiple specialized AI models in real-time, creating a cohesive system that understands, decides, and acts autonomously.
Edge Processing: 90% of decisions made locally within 50ms
Context Awareness: Understands weather, traffic, and special events
Explainable AI: Natural language explanations for every decision
The Neural Network of Smart Cities
Processing 1.2M decisions per second across 50+ cities
Live Scenario:
Emergency Response
“It’s 3AM in Synevyr, snowy, -18°C. A truck skids. YOLOv9 detects it. Qwen flags high risk. RL agent boosts light to 100%. DeepSeek logs the event. Gemma explains why the light was on — in plain Ukrainian.”
Sensor Activation
Radar detects vehicle moving at 62km/h in snowy conditions
Object Detection
YOLOv9 identifies truck with 94% confidence, calculates trajectory
Risk Assessment
Qwen 3-VL analyzes scene: icy road, poor visibility, high risk
Light Adjustment
Reinforcement learning agent boosts light to 100% illumination
Event Logging
DeepSeek R1 generates detailed report with risk factors
Operator Query
Real-Time Decision Making
From detection to action in under 200ms
High Risk Detected
Vehicle skidding detected • 94% confidence
Models
& Metrics
Specialized AI components working in concert for optimal performance
Object Detection
YOLOv9 achieves state-of-the-art performance in real-time object detection
≈ 74%
18ms
Jetson, NUC
Image Reasoning
Qwen 3-VL provides contextual understanding of visual scenes
±6%
92%
88% AUC
Language Q&A
Gemma 3 delivers instant, accurate responses to operator queries
< 0.8s
28+
Yes
Reports & Forecast
DeepSeek R1 generates actionable insights from operational data
2.4M
60-80%
±3%
Dim Decision
Reinforcement learning optimizes lighting levels continuously
60-80%
12,500+
100%
Maintenance Prediction
XGBoost models predict failures before they occur
0.91
14.5 days
42%
RURACTIVE Challenge 18 Compliance
| Criteria | Response |
|---|---|
| Road Safety | YOLO + Qwen detect and reason in real-time |
| Energy Saving | RL agent + DeepSeek optimizations |
| Community Trust | Natural language summaries via Gemma |
| Cost-Efficiency | Retrofit model, low OPEX |
| Maintenance | Predictive alerts + optimized field routing |
Security &
Edge Optimization
Enterprise-grade security meets edge efficiency
AES-128 Encryption
All sensor data and communications are encrypted end-to-end with military-grade encryption.
Delta OTA Updates
Updates as small as 200MB, minimizing bandwidth usage while keeping systems current.
Privacy Filters
No face data stored – only anonymized object detection metadata retained.
Edge Hardware
Runs efficiently on Jetson Nano/Orin and single GPU NUC devices.
Secure by Design
From silicon to cloud
Deep Integration: Sensors + AI + Speech
The AREYLight AI Engine creates a seamless pipeline from raw sensor data to actionable insights and natural language interaction.
Sensor Fusion: Lux, motion, and voltage data feed directly into DeepSeek R1 for system health monitoring
Visual Pipeline: YOLOv9 object detection streams to Qwen 3-VL for contextual reasoning
Natural Interface: Gemma 3 enables operators to query the system in plain language
AREYLight AI Assistant