Prevent Accidents with Real-Time Fatigue Monitoring
Detect driver drowsiness, distraction, eye-closures, and yawning instantly using edge-hosted computer vision. Protect your logistics operations and drivers without costly cellular video streaming or data residency issues.
The High Cost of Driver Fatigue
Driver fatigue and distraction are responsible for over 20% of commercial transport accidents globally, causing costly asset loss and catastrophic liabilities.
High Accident Liability
Micro-napping and road distraction lead to immediate multi-million dollar collision risks, elevated insurance premiums, and operational downtime.
Severe Bandwidth Overhead
Streaming continuous, high-definition video feeds from hundreds of vehicle cabins to cloud APIs is completely cost-prohibitive over cellular networks.
Poor Night Performance
Basic dashcams and legacy alert systems fail to spot fatigue during night shifts or in low-lighting conditions, which is exactly when micro-sleeps occur.
How Driver Monitoring Works
Our edge AI system analyzes face geometry locally inside the vehicle dashboard computer, triggering instantaneous warnings without needing network connections.
Infrared Video Stream
An in-cab camera equipped with infrared illumination captures the driver's face, working reliably under bright sunlight, shade, or total darkness.
Edge Face Geometry
A highly optimized local neural network tracks facial landmark coordinates to calculate eye aperture ratios (PERCLOS) and yawning frequencies at 30+ frames per second.
In-Cab Audible Alert
If eye-closure limits or distraction flags are breached, an immediate audible alert triggers, prompting the driver while logging the event payload to local storage.
Want to see this running on your fleet's dashcam feeds?
Share a 30-second video clip from any cab-mounted camera and our engineers will demonstrate the fatigue detection output and landmark tracking overlay.
Request a Free Detection AnalysisBuilt for Industrial Fleets
AdaptNXT builds custom edge computer vision pipelines to monitor driver behavior across demanding industrial deployments.
Freight & Logistics
Implement automated fatigue monitoring for long-haul freight operations to prevent vehicle damage, cargo loss, and insurance liability.
Public Transit
Secure city bus and coach transit networks against driver sleep-deprivation accidents with real-time distraction alerts.
Mining Operations
Protect haul truck drivers operating under highly repetitive routes and harsh underground or quarry conditions.
Heavy Machinery
Integrate distraction trackers into construction cranes, port terminals, and plant machinery controls to secure work sites.
Edge AI Capabilities
Driver Privacy Assured
Because the AI model runs locally on edge devices inside the vehicle, video data never leaves the cabin, ensuring full driver union and privacy compliance.
Instant Alerting (< 200ms)
Zero round-trip cloud lag. The system triggers in-cabin alarms immediately upon detecting threshold fatigue signals to prevent imminent crashes.
Low-Light and NIR Support
Our computer vision model supports Near-Infrared (NIR) video streams, performing accurately in pitch-black cabins or under rapid lighting changes.
{
"status": "active_monitoring",
"metrics": {
"eye_aperture_ratio": 0.24,
"perclos_30s_ratio": 0.31,
"yawning_frequency_1m": 2
},
"alerts": {
"drowsiness_detected": true,
"distraction_flag": false,
"alert_sound_triggered": true
},
"timestamp": "2026-07-03T10:28:14Z"
}Frequently Asked Questions
Quick answers about our edge-hosted driver fatigue monitoring capabilities.
The system works with standard Near-Infrared (NIR) vehicle cabin cameras. NIR is necessary to track eye states accurately in pitch-black conditions or under direct sunlight, bypassing shadows and sunglasses.
No. The system performs all face landmark tracking and video processing locally on the in-cab hardware. No video streams are uploaded to our servers, keeping driver privacy protected.
The computer vision model tracks eye closure duration (PERCLOS), yawning frequency, head tilt/orientation, and gaze distraction flags (such as looking down at a mobile phone).
If the system detects eye-closures or yawning exceeding safety thresholds, it immediately triggers an audible alarm in the cabin and sends a lightweight warning log (JSON packet) to your fleet dispatch server.
The model is optimized to run on low-power edge compute blocks such as NVIDIA Jetson Nano/Orin modules, or quantized to run on standard ARM-based cabin display units.
Secure Your Fleet with Edge Computer Vision
Reduce fatigue-related accidents by over 80%. Partner with AdaptNXT to deploy local driver monitoring devices into your transport grid.