Discover how Motive’s safety experts are reducing operational headaches, narrowing managers’ focus, and making North American roads safer.

Fleet operators know that on the road, swift and precise AI detection of unsafe behavior isn’t just crucial — it’s lifesaving. For instance, Motive found in a preliminary study that fleets using AI Dashcams reduce accidents by 57% within 4 months of regular coaching. AI empowers businesses to improve driver and passenger safety, prevent accidents, and minimize the human and financial costs of unsafe driving. 

Consider this: Together, driving behaviors like fatigue, illness, cell phone use, and eating behind the wheel are a leading cause of fatal collisions involving large commercial vehicles. With the power of accurate AI detection, who knows how many of these collisions could have been prevented?

Reliable AI detection enables safer performance

At Motive, accurate and reliable AI isn’t just a product feature, it’s an obsession. We know how important it is. But don’t just take our word for it. Here’s what the Director of Safety and Compliance at Groome Transportation had to say about the impact that Motive AI has had on driving performance:

“The accuracy of that AI detection means fewer false alerts for my drivers. When the dash cam alerts drivers about a critical event, they know it matters.”

Never content with the status quo, our product team is always innovating. We’ve been continually testing performance over the past few years, and the results reflect what we hear from people in the industry — our competitors’ camera systems often deliver late in-cab alerts or none at all, and are plagued by false positives. False positives occur when AI incorrectly identifies a normal, safe situation as a risk.

This erodes trust as AI wrongly tags safe behaviors as risks. The result? Drivers ignore alerts and persist in unsafe behaviors. Safety Managers, meanwhile, report spending hours daily cleaning up errors in other systems, preventing them from doing more value adding, strategic work tackling safety risks and rising costs.

Motive successfully alerts drivers 3-4x more than leading competitors, and our AI is at the core of this achievement. But there’s another secret weapon at our disposal — our 300-person Safety Team, who ensure that our AI models remain the best in the industry. 

Instead of making you sift through endless safety events, we’ve built a team with a highly evolved feedback mechanism to validate AI behaviors, eliminate false positives, and make collision detection more precise. The team’s insights also drive our R&D efforts for new AI models. Let’s explore their pivotal role.

Validation of AI behaviors: Removing false positives

Think of the Motive Safety Team as an extension of your team, streamlining your safety program by spotlighting crucial safety events and removing false positives. The team reviews thousands of videos daily, all within seconds of the event, ensuring events are available immediately for customers. 

If our AI Dashcam generates an event with any uncertainty, our team reviews and validates the event. Any errors are corrected and used to enhance the AI model.

In this example, AI identified an object as a cell phone in the driver’s hand, triggering a Cell Phone Usage event. Uncertain of what it was seeing, the AI automatically flagged the event for review. Our team realized the driver was holding a handheld radio and corrected the tag.

In this example, AI labeled an incident as a Seat Belt Violation. Yet, without full context, it missed that the vehicle was in a parking lot where seat belts weren’t necessary.

Here, the video was identified as a Stop Sign Violation. However, the vehicle had changed course before reaching the sign.

Through incident reviews and retagging, the Motive Safety Team team enhances the R&D process, improving the accuracy of our AI models over time. A Motive-sponsored Virginia Tech Transportation Institute (VTTI) study confirmed our progress. The study showed that the Motive AI Dashcam alerted drivers to unsafe driving 86% of the time, achieving the highest alert rates for six unsafe behaviors compared to leading competitors. Unlike others, our AI delivers reliable, timely information.

Improving the accuracy of collision models

To help drivers and initiate insurance claims, safety managers need immediate accident notification. This footage can offer drivers instant exoneration and be presented as evidence if litigation arises.

The dash cam footage proved invaluable when a Sabel Steel truck was involved in a multi-vehicle accident on Interstate 10 in Mobile, Alabama. “Following the incident, I received a collision alert email, enabling me to review the dash cam video on Motive’s platform and share it with local authorities and our insurance to vindicate our driver.” Consequently, Sabel Steel saved approximately $200,000 and avoided months of litigation.

Our collision model detects over 99% of severe collisions, such as rollovers and jack-knifes, along with minor events like fender benders and object hits. HD video is uploaded seconds after an accident occurs, and managers are notified immediately.

We achieved this accuracy by training our collision model on over 10,000 crashes. Furthermore, 1-2% of the 10,000 possible collision videos reviewed daily by the Motive Safety Team are collisions, providing data for continuous refinement. The team reviews and tags collision severity, carefully differentiating between actual collisions, near collisions, and possible collisions, which can look quite similar. That’s why it’s so important for the Motive Safety Team to review the videos thoroughly and provide their input.

In the first video, the driver hard brakes to avoid a collision, so it’s tagged as a Near Collision. While hard braking often triggers false positives, our team excels in identifying genuine incidents.

The second video captures a sudden jolt while the vehicle is backing up, indicating a possible rear collision. It’s marked as a Possible Collision.

In the small chance that collisions are missed, two valuable feedback mechanisms help refine Motive’s collision model: collisions submitted to our support team and customer-recalled footage. By analyzing these real-world inputs, the model gains a deeper understanding of different collision scenarios and becomes more precise.

Developing new, accurate AI models

For AI models to be effective, it’s essential that they capture a vast array of real-life scenarios. Motive’s Safety Team helps develop new AI models and improve their accuracy by identifying these uncommon, outlier scenarios. 

Take a hard-to-spot Distraction event, for example, where the camera’s angled installation creates the illusion of a driver looking away from the road. The safety team adds context to the event. That context is then fed into the AI model so the model can be retrained and detect these scenarios in the future.

For one of the newest behaviors detected by Motive’s AI, Stop Sign Violation, the team trained the model to avoid different outlier scenarios. Consider the scenario where a vehicle passes a stop sign located on a nearby intersecting road. The AI initially detects this as a Stop Sign Violation. However, upon review, it becomes clear that the alert was not applicable to the vehicle’s actual route. As the team provides consistent feedback on similar events, the AI learns to stop flagging them as Stop Sign Violations.

The Motive Safety Team complements human expertise with AI precision. The team’s work is invaluable to preventing accidents and creating safer roads. The more precise the AI detection, the more protected your people will be. Learn about the most recent enhancements we’ve made to our AI. Take a tour to learn more about the Motive AI Dashcam and Driver Safety product today.