Fatigue is dangerous because it is gradual and invisible until too late. Detecting it means reading small, early signals — eye closure, head nod, gaze, steering — in the cab, in real time, and being right often enough that the driver actually trusts the alarm rather than switching it off.
| Aspect | Fatigue | Distraction |
|---|---|---|
| Onset | Gradual, over minutes | Sudden, over seconds |
| Primary signal | Eye closure, head nod | Gaze leaving the road |
| What it means | The driver should not continue | The driver must re-focus now |
| Right response | Escalating alert, rest break | Immediate attention prompt |
| Failure if missed | Micro-sleep at speed | Eyes-off-road collision |
| Time to act | Seconds of warning if early | Almost none once it starts |
The detection itself — find the face, track the eyes, measure how long they stay closed — is the part that sounds hard and is largely solved. The hard part is being right often enough, in the conditions a cab actually presents, that the system earns its place. A driver who is warned wrongly a few times learns to ignore or disable the alarm, at which point the system is worse than nothing, so the entire design is governed by the cost of a false alarm as much as by the cost of a miss.
That pushes the whole thing onto the edge, inside the cab. A fatigue alert that depends on a cloud round trip is useless when the vehicle is in a tunnel or a dead zone, and streaming a driver’s face to a server is a privacy problem that operators and regulators will not accept. So inference runs on the device, the video never leaves it, and only events — not footage — reach the fleet. Working in the dark is not optional either: fatigue happens most at night, so the camera sees by infrared and the models have to hold up through darkness, glare, sunglasses and a mask.
Finally, drivers differ. A fixed threshold for “eyes closed too long” will nag one driver and miss another, so the system calibrates a baseline per driver and watches for deviation from it rather than from a global number. Fatigue and distraction are then kept separate, because they need different responses — a rest break versus an immediate re-focus — and treating them as one alarm is how a system loses the driver’s trust.
They show up in different signals and need different responses. Fatigue is gradual and shows as eye closure and head nodding; the right response is an escalating alert and a rest break. Distraction is sudden and shows as gaze leaving the road; the right response is an immediate prompt to re-focus. Treating them as one alarm gives the wrong response to at least one of them, which is how a system loses the driver.
For latency, privacy and availability. A fatigue warning that waits on a cloud round trip is useless in a tunnel or a dead zone, and sending a driver’s face to a server is a privacy problem operators and regulators reject. Running inference on the device keeps the response immediate, keeps the video in the cab, and works with no connection at all.
By fusing several signals rather than trusting one, by calibrating a baseline per driver instead of a global threshold, and by making the models robust to glasses, glare and darkness. The false-alarm rate is treated as the primary constraint, because a driver who is warned wrongly learns to ignore or disable the system, and then it protects no one.
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