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Dynamic performance-based adaptive monitoring model for dash camera systems using early-trip and rolling performance scoring

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ADomi-10564
Original Poster

Current in-cab and telematics camera systems generally apply uniform monitoring and alerting regardless of driver performance trends or real-time signals. While this ensures consistent safety coverage, it can also create unnecessary friction for consistently safe drivers and increase operational load for fleet managers.

 

In high-turnover, high skilled labor, or mixed-skill fleet environments, this can lead to reduced driver trust, alert fatigue, and inefficient coaching prioritization.

 

Proposed concept

 

Introduce a dynamic, performance-based monitoring model that continuously adjusts in-cab alerts and inward-facing camera monitoring intensity based on:

 

  • Early-trip driving signals (first 10 to 30 minutes of each drive)
  • Rolling driver performance trends updated daily or weekly

 

The goal is not to reduce safety coverage, but to improve how risk is interpreted, prioritized, and operationalized within existing fleet workflows.

 

Example dynamic model (conceptual)

 

Driver classification would be continuously updated rather than fixed:

 

Top 20% (consistently low-risk drivers):

 

  • Determined through sustained safe driving performance over time
  • No routine inward-facing monitoring review workflows triggered
  • In-cab alerts only when high-severity events occur
  • Tier adjusts dynamically based on recent performance changes

 

Middle 60% (variable or developing performance):

 

  • Standard in-cab alerts
  • Selective inward-facing review based on event severity, pattern changes, or early-trip risk indicators
  • Dynamic movement between tiers based on rolling performance trends

 

Bottom 20% (higher-risk or unstable performance):

 

  • Full in-cab alerts
  • Active inward-facing monitoring review workflows
  • Prioritized coaching and intervention based on both early-trip signals and accumulated performance patterns

 

Dynamic logic

 

The system would continuously adjust tiering using:

 

  • First 10–30 minutes of driving as an early-trip risk signal window
  • Rolling weekly performance trends for stability and consistency weighting

 

This enables the system to respond to changes in performance quickly rather than relying on static classification.

 

Expected benefits

 

  • Reduced unnecessary surveillance perception for consistently safe drivers
  • Faster identification of emerging risk patterns through early-trip signals
  • Lower alert fatigue for safety and operations teams
  • More efficient coaching workflows focused on meaningful performance changes
  • Improved driver trust and retention
  • Better alignment of monitoring intensity with current operational risk

 

Key principle / constraint

 

The system maintains continuous full video and telematics capture for all trips to preserve safety, compliance, and liability coverage.

 

The adaptive model applies only to how information is interpreted and operationalized within fleet workflows, including:

 

  • Alert intensity and frequency
  • Inward-facing event surfacing and review prioritization
  • Coaching workflow routing and escalation logic
  • Dynamic performance tier assignment

 

Operational context

 

This model is most relevant for fleets with:

 

  • High driver turnover
  • Mixed experience levels
  • Service-oriented or customer-facing operations
  • High frequency of low-severity events that dilute coaching focus 
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6 Replies

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EishiFUN
Geotabber

Edited by EishiFUN

Hello @ADomi-10564​ ,

 

Thank you for giving us this feedback. This is really well thought out and thorough. I would encourage you to give this feedback to our Feedback Hub. This way our product team can see and review all the details you shared and it could help influence updates they make.

 

If you need anything else feel free to reach out. We are happy to help.

 

Have a great day!

Eishi FUN

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ADomi-10564
Original Poster

Curious if others are seeing alert fatigue with in-cab camera systems, or if anyone has come across dynamic or behavior-based driver tiering in real use yet?

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EishiFUN
Geotabber

I wonder if @IvanAllegreMM-9898​  can share anything about what he has learned since using GO Focus

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IvanAllegreMM-9898

Hello @EishiFUN​  and @ADomi-10564​ , thanks for bringing me into this great discussion!

 

To address your first question: yes, alert fatigue is a very common issue. In my experience working with clients ranging from small fleets to large corporate enterprises, a constant barrage of static alerts frequently leads to drivers getting frustrated and viewing the camera as an enemy rather than a helpful tool, which reduces the effectiveness of the in-cab coaching system.

 

Your proposed dynamic model is spot-on. I'd love to add another critical variable to this dynamic approach: adjusting alert sensitivity based on real-time vehicle telemetry, specifically vehicle speed.

 

I recently carried out tests over 2 weeks with the GO Focus Plus. During my operational analysis, I identified a risk regarding the time thresholds the camera requires to trigger a cell phone use alert.

Here is what I found:

 

  • Tests showed that if a driver uses their phone "quickly" (Changing a song, viewing a message, actions that take >1s), the system does not detect it.
  • I mapped out the meters a unit travels "blind" based on the time it takes the AI to validate the event, evaluating Low (7s), Medium (5s), and High (1s) sensitivity settings.
  • For example, traveling at 90 km/h with a 5-second validation time (Medium Sensitivity) results in advancing 125 meters without full attention to the road.
  • Therefore, I believe this sensitivity rule would benefit greatly by being dynamic and becoming stricter as the unit's speed increases.

 

Integrating your idea of rolling driver performance trends with real-time dynamic thresholds (like shrinking the cell phone validation window as vehicle speed increases) would make these systems much more efficient and less frustrating in daily operations.

 

Looking forward to seeing how this concept evolves. Happy to share more about my testing if anyone is interested!

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EishiFUN
Geotabber

Thanks for sharing your testing with us Ivan!!

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ADomi-10564
Original Poster

Hello @IvanAllegreMM-9898​ 

 

Thanks for sharing this, really interesting breakdown.

 

I'd actually be very interested in hearing more about your overall testing if you're open to it, especially since you mentioned you carried out the GO Focus Plus evaluation over a couple of weeks.

 

What I'd be curious about is how that testing played out across different scenarios, and what you ultimately landed on in practice in terms of sensitivity settings once you accounted for the tradeoffs you identified around detection time, validation delay, and vehicle speed.

 

Also interested in whether anything meaningfully changed operationally for drivers or coaching feedback once you started working through those settings in real use.

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