# AI Fatigue Insights

Program insights

##### Snapshot

Sample 30-day metrics:

- Total fatigue alerts: 4,218.
- Microsleep events: 612 (14%).
- Repeated high-risk drivers (≥ 3 events): 7.
- Average alert response time: 3.4 min.

##### Narrative insight

Example AI-generated narrative you can later replace with live output:

“Over the last 30 days, overall fatigue alerts increased by 8%, driven mainly by long-haul night routes. Microsleep events are concentrated in a small subset of drivers who frequently exceed 4 hours of continuous driving without a break.

Urban fleets show fewer high-severity alerts but more camera/lighting issues, which occasionally suppress detection accuracy during night shifts. When cameras are healthy, the PoseNet + OpenCV pipeline captures early signs of fatigue (head pose + eye-closure) roughly 12–18 minutes before lane-keeping interventions trigger.”

Suggested operational changes

- Introduce stricter break enforcement for routes with repeated microsleep events.
- Schedule targeted night-time camera checks for urban vehicles with detection gaps.
- Use AI Safety Coach to provide individualized feedback to the top 10% of at-risk drivers.

Prompt that produced this insight (example)

`“Summarize fatigue and detection performance for the last 30 days, highlight any risky patterns, and recommend three concrete changes for fleet safety operations.”`

##### Alert & workload trends

Use this panel for charts later:

- Long-haul teams handle most critical alerts but also show higher workload overnight.
- Regional fleets see spikes in medium-risk events near shift handoff times.
- Urban fleets have short trips but more frequent low-severity pose alerts.

##### Prompt ideas

Questions operations leaders might ask the AI:

- “Which routes show the highest microsleep risk after midnight?”
- “How does fatigue risk differ between experienced and new drivers?”
- “What are the top 5 root causes behind our recent critical fatigue alerts?”