What is a driver monitoring system?
A driver monitoring system (DMS) is a camera based safety system that monitors a driver’s behavior and warns or alert them when they become distracted or drowsy for a period of time long enough to lose situational awareness or full attention to the task of driving
What is the goal of a driver monitoring system?
In addition to continuously watching, notifying, and alerting drivers to focus on the road, maintain safe following distances, and travel at safe speeds. Driver monitoring systems are designed to:
- Alert drivers before collisions happen; that is, avoid collisions rather than report on the collision after the fact
- Helps drivers improve their driving; especially newer drivers who are statistically several times more likely to be involved in collisions
- Confirm drivers are following processes and procedures outlined in driver safety programs
- Reduces operational costs, lower insurance spend, achieve safety metrics, exonerate drivers, and save lives
Why are driver monitoring systems needed?
When a driver is engaging in one or more distracted driving behaviors, the result is the same: heightened risk of a collision, injury, or even fatality. There’s little argument that distracted driving is one of the fastest growing threats to safe driving today and cause of collisions in the United States. Nearly 95% of serious traffic collisions are due to human error, with over 70% of commercial fleet collisions involving distracted drivers. The National Safety Council reports on a typical day, more than 700 people are injured in distracted driving crashes. These statistics are true indicators of how safely drivers are using the roadways and why keeping commercial fleet drivers and bystanders safe on the road is becoming a bigger and more costly challenge for many fleet managers and safety leaders.
What are the challenges of implementing a driver monitoring system?
The biggest challenge fleet and safety managers face in implementing a driver alert system is providing drivers with the right information at the right time, without intruding on their privacy or professionalism.
1. Real-time Alerts - Many video telematics and dash cam solutions require driver video to be uploaded to the cloud for analysis (i.e. human review) before any distraction determination is made. This time lag, or latency, resulting from data transmission from the vehicle to the cloud and back again prevents real-time alerting. This means the driver doesn’t get a chance to act in time to prevent the incident, and worse, the supervisor knows something has happened even before the driver does.The good news? Artificial intelligence enables this. AI can deliver real-time feedback before an incident occurs, giving drivers a chance to avoid collisions instead of just triggering and reporting that an event has already occurred.
2. Driver Privacy - Drivers fear in-vehicle video will be used against them, and they will find ways to block the system from working as intended. Implementing a driver alert system requires a DRIVER-FIRST approach, versus a "report and coach later" approach. When evaluating different driver and fleet safety systems, do not be confused by vendors that spin technology deficiencies into “features” that ultimately limit driver adoption and loyalty by performing live streaming or unannounced drop-ins . Require your vendor’s platform to perform real-time AI in the vehicle, without requiring video upload to the cloud to detect driver distractions or imminent collisions. Use video to coach and exonerate, not to surveille!
How Nauto’s AI driver monitoring system works
Nauto uses AI sensors in the vehicle to detect driver movement, gaze direction/attention, vehicle activity, traffic conditions, and other contextual data to make real-time decisions about imminent risks can provide on average a 40%-60% reduction in collision frequency and collision-related costs. AI-powered Driver Behavior Alerts technology is a proven way fleets can combat distracted driving by audibly alerting drivers as soon as they’ve become distracted for an extended period of time. This allows the driver to correct their behavior proactively, instead of relying on retroactive coaching weeks after an event. By analyzing billions of data points from over one billion AI-analyzed video miles, Nauto’s machine learning algorithms continuously improve and help to impact driver behavior before events happen, not after.