Project 37 · Automotive / Fleet Safety

Driver Behavior Monitoring System

In-Cabin Coaching-First Driver State Analytics

Industry
Automotive / Fleet Safety
Services
In-Cabin Sensing ML Analytics
TRL
2 → 7
Duration
7 months
Technologies
In-cabin camera driver-state ML CAN telematics
On-device in-cabin perception
Figure 1 — On-device perception with explicit driver-privacy boundary.
Driver coaching app screen
Figure 2 — Coaching app with framing rules for actionable feedback.
Fleet behavior trend report
Figure 3 — Score distribution shift across the fleet over time.
Real-world Driver Behavior Monitoring System deployment
Figure 4 — Real-world deployment in a fleet cab.

Project background

Fleet safety depends heavily on driver behavior — fatigue, distraction, aggressive driving — yet few operators have structured visibility into it. The client wanted an in-cabin system that measured and coached, not just surveilled.

Challenge

Balancing genuine safety insight against driver privacy and acceptance, and turning raw behavior signals into coaching that drivers respond to rather than resent.

Approach & solution

We built an in-cabin monitor that analyzes driver state while keeping video processing local where possible. Events are summarized for the driver in-app and rolled up for fleet managers in aggregate. Coaching framing was developed with operator input to avoid punitive tone.

Results & benefits

Fleets saw measurable shifts in behavior patterns over the first weeks of deployment, and driver surveys indicated acceptance once the coaching — rather than surveillance — framing was clear.

Have a project in mind? Let's build it.

We reply within one business day.

Start a project