Regardless of the route or road, many drivers have experienced fatigue at some point while on the job. Driver fatigue is an extremely dangerous condition. Serious accidents can occur when drivers have trouble concentrating and start to doze off at the wheel. The Federal Motor Carrier Safety Administration has released updated Hours of Service (HOS) regulations to help reduce the number of consecutive hours drivers spend operating vehicles. However, compliance doesn’t always equal safety. Drivers can be 100 percent compliant with HOS regulations and still experience tiredness that affects their ability to perform.
Fleets can tap into the power of artificial intelligence and machine learning to improve safety and protect the driver, fleet, and the general public. Fleets can use the granular data available from ELDs and other Omnitracs solutions to gain a deeper understanding of what causes drivers to become fatigued.
Artificial intelligence and machine learning take it a step further by automatically highlighting drivers who could be fatigued after analyzing data points such as:
- Number of hours worked
- Time of day (or night) they’re driving
- Number of breaks taken
- Time-to-lane-crossing
- Speed variations
Empowered with these predictions, employers can speak with drivers then readjust schedules, re-assign loads to non-fatigued drivers if needed, and implement changes fleet-wide to ensure drivers are staying alert and focused on the road.
To learn more about how you can combat driver fatigue using predictive analytics, visit this page and check out the Driver Fatigue Model.
This is part three of a three-part series:
Improving driver retention with artificial intelligence & machine learning
Improving driver safety with artificial intelligence and machine learning
Reducing driver fatigue with artificial intelligence and machine learning