Automotive Cabin Monitoring
Custom rPPG for In-Cabin Camera Systems
We build custom preprocessing pipelines and train rPPG models specifically for automotive NIR and RGB in-cabin cameras. Drowsiness detection, cardiac event alerting, and continuous driver health monitoring — engineered for the unique challenges of the vehicle cabin environment.
Every vehicle cabin is different. Sun glare, vibration, partial occlusion, and rapidly changing ambient light all degrade generic rPPG models. We build models tuned to your exact camera hardware, mounting position, and target processor.
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What We Build
Custom NIR/RGB Preprocessing
Algorithms that handle rapidly changing ambient light, sun glare, and shadows unique to vehicle cabins.
Vibration-Resilient Signal Extraction
Motion artifact compensation tuned for road vibration, bumps, and driver movement patterns.
Drowsiness & Fatigue Detection
Custom models that detect microsleep, eyelid droop patterns, and attention degradation from cardiac signals.
Cardiac Event Alerting
Real-time cardiac anomaly detection for driver safety — arrhythmia patterns, sudden HR changes, vasovagal indicators.
Embedded Processor Optimization
Models optimized for automotive-grade SoCs — Qualcomm SA8155P, NXP S32G, Texas Instruments TDA4VM and similar.
ADAS Integration
Direct output to Advanced Driver Assistance Systems — CAN bus compatible, AUTOSAR-ready data formatting.
Built for Automotive-Grade Requirements
Every component is validated against automotive environmental and performance standards. From temperature extremes to real-time processing constraints, our custom builds meet the demands of production vehicle deployment.
Camera Types
NIR 940nm, RGB visible-light, ToF depth sensors, multi-spectral arraysProcessing Targets
Qualcomm SA8155P, NXP S32G, TI TDA4VM, NVIDIA OrinFrame Rates
15-60 fps optimized for each camera moduleMeasurement Time
Sub-10-second continuous monitoring loopsOutput Formats
CAN bus messages, AUTOSAR-compliant, custom JSON/protobufEnvironmental Range
-40C to +85C validated preprocessingAutomotive Cabin Monitoring FAQ
Common questions about custom rPPG for in-cabin camera systems
How does rPPG work with NIR cabin cameras?
940nm NIR illumination penetrates the skin and reflects off subcutaneous blood vessels. As blood volume changes with each heartbeat, the reflected NIR intensity shifts measurably. Our custom preprocessing separates this blood volume pulse signal from ambient IR noise, dashboard reflections, and other interference unique to vehicle cabins.
Can you handle sun glare and rapidly changing lighting?
Yes. We build custom preprocessing algorithms trained on diverse automotive lighting conditions including direct sunlight, tunnel transitions, night driving with oncoming headlights, and dappled shade from trees. The preprocessing normalizes these rapid illumination changes before signal extraction, maintaining measurement continuity across all conditions.
What automotive processors do you support?
We optimize for all major automotive-grade SoCs including Qualcomm SA8155P, NXP S32G, Texas Instruments TDA4VM, NVIDIA Orin, Renesas R-Car, and Intel Mobileye EyeQ. Our optimization process profiles your target processor, quantizes the model appropriately, and validates inference latency meets your real-time requirements.
How does this integrate with existing ADAS systems?
We output directly to CAN bus with AUTOSAR-compliant data formatting. The vital signs data integrates with your existing Driver Monitoring System (DMS) pipeline, feeding into ADAS decision logic alongside gaze tracking, head pose, and other driver state signals. We support standard DMS interfaces and custom integration protocols.
What's the timeline for a custom automotive build?
Initial model delivery takes 2-4 weeks from receiving your camera hardware and sample data. We then iterate with in-vehicle test data — typically 2-3 tuning cycles covering diverse driving conditions, driver demographics, and environmental scenarios. Full production-ready delivery is usually 6-8 weeks end to end.
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