In light of April being Distracted Driving Awareness Month and the fact that each year there are over six million collisions in the U.S. (leading to three million people injured and over 36,000 deaths -- we thought it would be the perfect time to discuss how to measure driving risk in your fleet, the role of driver distraction and other driving behaviors in causing collisions, and how the right driver and fleet safety platform can help you reduce fleet risk and collision loss.
According to a study by NHTSA, 94% of all collisions are caused by human errors like distraction, speeding, tailgating, improper turns, and others. Detecting the human behaviors that cause driver error and measuring risk can be a very complex and diverse issue in commercial fleets. Traditional telematics-based methods track certain events like hard braking and acceleration, but don’t capture the biggest risk factor - driver behavior. To effectively reduce fleet driving risk, it is necessary to capture critical, risky driving behaviors in real-time, understand those behaviors in the context of the situation on the road, automatically train and improve driving behavior, and identify the highest risk drivers for additional coaching. So let’s break down the core fleet risk factors into three categories: driver risk, fleet management and vehicle risk, and external risk.
1. Driver risk
This data provides you the ability to predict a driver’s likelihood of having a collision and the severity of those collisions.
- High risks events
- Traffic violations
- Aggressive driving
2. Fleet management and vehicle risk
This data provides you with the ability to allocate risk to drivers based on vehicle class, staffing, and other operational factors controlled by fleet managers.
- Vehicle risk profiles: class, features, technology, matching driver risk with vehicle risk
- Operations planning: staffing and route planning/optimization
- Operational management and work-induced distractions
3. External risk
This data allows you to allocate risk to external risk factors and will provide an improved understanding of the risk a driver is dealing with, which for the most part are beyond their control.
- Environmental and weather factors
- Road type and location
- Road conditions
Based on our AI and computer-vision powered analysis of over one billion visual driving miles, Nauto has identified a cohesive set of risk variables from the above categories, measured these variables’ correlation to collisions by type, and developed the technology for reducing fleet driving risk, collisions, and losses.
Nauto achieves fleet risk reduction in four different ways:
- Direct intervention - Helping drivers avoid collisions by providing real-time in-vehicle alerts.
- Indirect intervention - Guiding the efforts of fleet managers and safety coaches to maximize positive impact on driver behavior through targeted coaching, training, reward/recognition, or corrective action.
- Intangible improvements - Due to in-vehicle alerts and coaching, there is observed improvement in driving that is different from direct and indirect interventions. For example, reduction in failure to yield to pedestrians due to reduced distractions.
- Improved management engagement and effectiveness - This is achieved through the integration of Nauto technology and metrics in safety programs.
The Nauto fleet and driver safety platform has established a proven correlation between risk and reduction in collision frequency, severity, and collision-related financial loss.. Based on powerful AI technology, sophisticated data science, and rigorous coverage of a majority of critical risk factors, Nauto customers experience as high as an 80% reduction in collisions and actual collision loss dollars. Most importantly, Nauto helps fleet drivers avoid collisions, saving human lives!
To learn more about Nauto’s methodology and approach when it comes to risk., join us for the upcoming Reduce Fleet Driving Risk and Collisions webinar on May 6th at 11AM PT. We’ll take a deep dive into the three important components of fleet risk management and show you how risk variables relate to specific collisions and how we can reduce specific collision types and associated losses.