# Driver fatigue dashboard

Generative UI by Hashbrown
Fleet
Window

Active vehicles

### 128

+12 vs. yesterday

High fatigue alerts

### 9

3 unacknowledged

Average blink rate

### 14/min

Within expected range

Cameras calibrated

### 94%

7 need re-check

##### Live fatigue panel

Sample data

Driver | Vehicle | Route | Risk | Last event
--- | --- | --- | --- | ---
Alex Chen ID D-102 | Truck 18 · Long-haul | LA → Phoenix | High | Microsleep detected 6 min ago
Maria Lopez ID D-214 | Van 08 · Urban | Downtown loop | Medium | Prolonged eye closure 18 min ago
Samir Patel ID D-331 | Truck 05 · Regional | Depot → City hub | Low | No events in last 2 hours

##### AI fatigue summary

Powered by Hashbrown

Example narrative (replace with live AI):

- High-risk alerts are clustered on long-haul routes between 02:00–05:00.
- Three drivers generated repeat alerts across multiple nights.
- Urban fleet shows fewer fatigue events but more camera-calibration issues.

Suggested operator focus

1. Contact the three drivers with repeat high-risk alerts for coaching and schedule review.
2. Review break compliance for long-haul routes, especially early-morning segments.
3. Schedule recalibration for cameras in vehicles with frequent detection gaps.

Open AI Safety Coach View fatigue insights

##### Alert & event trends

Placeholder for your charts (last 24h):

- Microsleep events peaked around 03:00 and 16:00 local time.
- Most alerts came from vehicles with > 4 hours continuous driving.
- Eye-closure alerts strongly correlate with lane-keeping corrections.

##### Camera & model health

Sample operational checks you can wire up:

- Video uptime: 97% across the fleet.
- PoseNet confidence < 0.5 for longer than 5 min: 2 vehicles.
- OpenCV face/eye detection failures: 4% of frames (usually due to lighting).