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ARTIFICIAL INTELLIGENCE

Why Predictive-AI Is Your Key to a Safer Fleet

Every day, your drivers face a wide range of distractions in the cab and risks on the road. Cell phones, drowsiness and fatigue, road conditions, other vehicles, pedestrians, and cyclists all add to the challenge of completing their shifts and getting home safe.

These distractions and risks can combine in a multitude of ways to make collisions more likely. Imagine, for example, a driver looking at a cell phone while traveling 40 miles per hour and approaching an intersection where another car is about to cross multiple lanes.

A collision could easily ensue, or it could be avoided — if the driver puts down the phone and re-focuses in time to hit the brakes. A driver alert that comes at the right time, with the right intensity, and is designed to help the driver do just that can help prevent the collision and all that may result: vehicle damage, injuries, fatalities, and costs.

Drivers in approximately 800 fleets are leveraging alerts like these — powered by Nauto’s predictive-AI — to help avoid collisions and make the roads safer for everyone. The results are telling: Nauto customers can reduce distracted driving, collisions, and losses by up to 80%.

Nauto's customers can reduce distracted driving, collisions, and losses by up to 80%. Predictive-AI gives your drivers the power to help prevent collisions, making results like these possible.

What is predictive-AI?

In-vehicle alerts that help prevent collisions are the most tangible output of predictive-AI, but they are just one part of what makes predictive-AI a uniquely powerful way to improve your fleet’s driver and vehicle safety.

When you understand what distinguishes predictive-AI, you’ll be ready to select the technology that helps your fleet achieve rapid reductions in distracted driving, collisions, and losses.

Nauto’s predictive-AI consists of:

  • An AI-native design and architecture to leverage cutting-edge research, algorithms and techniques.  
  • High-quality, real-world data collected from a diverse range of drivers, driving conditions, and geographies combined with sophisticated data science.
  • Advanced neural networks capable of multitasking and fusing input data from multiple sensors in real-time.

This combination unlocks important benefits that make your drivers and vehicles safer:

  • Highly accurate driver alerts that account for not only driver behavior but also vehicle motion and external risks— all in real time, all while respecting driver privacy.
  • Accurate detection of a broad spectrum of risks, including complex risks, such as drowsiness, and combined risks, such as distraction while approaching a vehicle or pedestrian.
  • A driver and vehicle safety solution that accounts for 91% of the driver behavior risks that cause a collision and is always improving.¹
  • Driver coaching that’s focused on the behaviors and situations that matter most.
  • Rapid, sustained reduction in distracted driving, collisions, and costs. Nauto can help reduce loss by up to $2,500 per vehicle per year, freeing up substantial resources to accelerate your business growth.

See Nauto’s predictive-AI in action in this video.


Let’s explore each component of predictive-AI in more depth and see how they work together.

AI-native design and architecture

You’ve probably seen fleet safety system providers adding AI to their solutions that were originally designed for other purposes, such as event recording or tracking the traditional ABCs (hard acceleration, braking, and cornering).

Though adding AI to an existing system can expand the features available, it won’t allow you to realize the full potential of AI the way an AI-native system will. An AI-native design and architecture means the devices and the entire solution are built to maximize what AI can do, incorporate cutting-edge AI algorithms and techniques, and update everything as new research emerges.

At Nauto, we’ve built our entire system, from the beginning, to be AI-native. And, we’re developing our own AI innovations specific to fleets. In fact, we now hold more than 20 patents on our predictive-AI technology.

What does this mean for you? It means you get a solution that can rapidly improve as technology advances.

It also means driver privacy protection, which can help with driver acceptance. Traditional fleet dash cam systems are designed and built first and foremost to record. Even when AI is added, the device’s fundamental purpose remains the same.

By building on an AI foundation, we’ve created a solution that is designed to guide the driver to safer behavior and watches the external circumstances but only records high-risk events and collisions. Drivers get highly accurate, valuable alerts that help them self-correct before either one occurs, so they can reduce risk on the road without feeling spied on.

These alerts, because they help drivers prevent collisions, are a key reason our customers start seeing results in weeks rather than months.    

Learn more about how our predictive-AI supports multiple aspects of driver and vehicle safety, management, and coaching in this video.

High-quality, real-world data plus sophisticated data science

Driving is complex, and understanding the broad range of factors that influence risk and the importance of each one requires the right data plus rigorous data science.

By the “right data,” we mean:

  • An extensive amount of data to capture relevant risk factors, including those that are highly risky but don’t occur very often.
  • Data collected from the real world, not a lab where people mimic certain driving behaviors.
  • Data that includes many instances of a risk factor, such as a driver looking at a cell phone for two seconds, for three seconds, for four seconds, and so on.
  • Data that includes many combinations of different risk factors, such as a driver looking at a cell phone for three seconds while drowsy and approaching a pedestrian.
  • Data from a diverse set of drivers across a diverse range of geographies and road and driving conditions.
  • Data that connects to outcomes, especially collisions and near-collisions.

Our predictive-AI has learned from approximately 1.3 billion visual miles that meets all these key criteria, and every month we’re gathering more than 100 million additional visual miles. This data is the first step in enabling Nauto’s predictive-AI to understand each risk factor in greater detail, how each one affects the chances of a collision, and how they combine.

The next step is leveraging sophisticated data science and active learning methods to hone in on the incremental data of interest to train our AI models. In other words, we’re using the data that’s most valuable for AI training rather than all the available data.

Perhaps counterintuitively, using all the available data is not the ideal way to train AI. For AI to learn to predict driving risk and prevent collisions, the situations that relate to that task need to make up a large-enough share of all the situations the AI sees. Showing the AI all the available data would mean showing it, in relative terms, too few high-risk, low-frequency situations. Trying to use all this data would also slow down the learning process.

Strategic data collection and rigorous data science also ensure we have sufficient data diversity, which is essential for the AI to understand drivers with different characteristics, drivers wearing hats, sunglasses, and masks, and drivers working at night and in inclement weather.

Together, the data and data science give our predictive-AI the power to learn from the situations you care about most and to do so quickly. This combination also means our predictive-AI can identify patterns that humans would never notice but do contribute to collision risk.

Nauto addresses 91% of the driver behavior risks that cause collisions, including cell phone use, smoking, speeding, tailgating, and drowsiness.¹

Advanced neural networks capable of multitasking and fusing multiple inputs in real-time

Driver behavior alerts can only help prevent collisions if they help your drivers take action before a collision occurs. That means the high-risk event needs to be identified early enough to sound an alert that can give your drivers a chance to respond.

Predictive-AI makes alerts like these possible because it leverages convolutional neural networks to build models that detect multiple risks and fuse data from multiple input streams in real time. In other words, the Nauto device is designed to guide the driver toward safer behavior, watch the road from multiple angles — even those that might be hard for the driver to see — and detect external risks like other vehicles, pedestrians, and cyclists. Nauto then fuses all this data almost instantaneously, interprets it, and sounds valuable, accurate alerts.

These alerts sound in real-time because our models efficiently multi-task and because they run in the vehicle — also called “on the edge.” Less-efficient AI would require multiple models to assess multiple risks. AI that doesn’t run on the edge would require data to be collected in the vehicle and then travel to the cloud for processing. Then, a direction to send an alert would go back to the vehicle. Running multiple models and sending data to the cloud and back take time that simply isn’t available when a collision is imminent.

Our highly efficient models mean we can detect combinations of risks, such as when a driver is distracted and approaching a pedestrian, cyclist, or other vehicle. In these cases, when the device detects a covered risk, it can sound an earlier, stronger alert than when the driver is distracted but traveling on an empty road.

See an example in this video.


Our predictive-AI can also detect complex risks that evolve over time, such as drowsiness. Learn more in this video.


We don’t just use multi-tasked models to maximize efficiency; we also constantly optimize our models using a process called quantization. The result: we can continue adding valuable features to the same Nauto devices rather than requiring frequent hardware upgrades.

Questions about predictive-AI and your fleet?

In this guide, we’ve highlighted the key distinguishing features of our predictive-AI and how it can help your drivers prevent collisions and help you see rapid reductions in collisions and costs.

We understand you may still have questions. We’re happy to answer them and to show you exactly how predictive-AI can help you address your fleet’s most important safety challenges. Simply contact us today, and we’ll schedule a time to talk.

Source:

1. Nauto internal analysis

Author bio: The Nauto Story Scout is always on the lookout for ways to help you improve driver safety, prevent collisions, and make our roads safer for all. The Story Scout learned many of its skills from the Nauto predictive-AI, which is always on the lookout for risks and distractions to help your drivers stay safe.

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