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artificial intelligence

Compliance is essential to how well a fleet operates. Unfortunately, recent statistics show alarming rates in several key areas including traffic law violations, health and safety. Distracted driving, speeding, and stop-sign violations account for a high volume of recorded accidents annually.

For a long time, the industry has looked toward traditional cameras to detect such violations. However, with the mounting road data, continuous analysis of such footage by humans is unsustainable. Netradyne is combining artificial intelligence with these cameras to tackle compliance through smart data analytics.

As innovation transforms every corner of transportation, from driver safety and retention to compliance, the technology we add to our fleets ultimately determines how well we can improve the operation of our fleets — and the core of our industry.

Legacy trigger-based technologies that rely on responses to events that have already occurred are quickly becoming outdated. The rapid development of artificial intelligence and its growing adoption by the transportation industry shows that this technology is providing a more advanced way to track and enforce compliance among fleets.


Artificial intelligence is a field of study which involves training computers to act intelligently enough to carry out traditionally “human” tasks in a faster and more efficient way. AI spans across several industries, especially in entertainment, tech and engineering where its presence is more visible.

Outside of transportation, platforms like Spotify and YouTube use AI to recommend similar posts for users based on previous content they watched or listened to. Advanced chatbots use AI to communicate with humans and some of these bots are modified to be handy assistants like Apple’s Siri or Amazon’s Alexa.

In the transportation industry, AI tends to be more functional. For example, Tesla cars sport an autopilot function allowing the car to self-drive while navigating changing road conditions, stop signs, turns and other road users.


Several modern AI fleet integrations tend to be a combination of cameras and sensors governed by an artificial intelligence center.

On their own, HD cameras capture high-quality images of road events for fleets that have them mounted on each vehicle to record road footage.

We know that having cameras is particularly useful as evidence after crashes occur. It is also used as a way to collect road data such as changing road conditions. When you combine cameras with artificial intelligence, not only do they capture images and video, but they also intuitively analyze recorded footage to recover an accurate account of events.

For example, let’s assume that legacy trigger-based systems that rely on cameras alone are a pair of human eyes. In this case, the eyes see everything that happens on the road and recall those events later. Based on this recollection, the fleet makes several improvements to its operations.

When you layer AI and edge computing on top of camera systems, you’re adding an entire nerve center. Cameras are like human eyes while the sensors function like sense organs and the AI acts like a brain. In the human body, when the sense organs detect any form of stimuli, a message along with imagery is sent to the brain. The brain immediately processes this data and comes up with an instant response to stimuli.

It’s the same with artificial intelligence. In an AI camera system, cameras and sensors act as data collectors and messengers that send information to the AI system. This system analyzes the data collected and advises the driver in real-time on the best actions to take.


AI is trained through the process of machine learning. This provides a computer the ability to continue learning until it is perfect at handling a particular task and prepared to handle any uncertain variables it may encounter.

Since data is the bedrock of all learning processes, AI is only as good as the data it is trained on. This is why functionality is limited without a good data collection system. Netradyne understands this fact and has mapped 3 billion minutes and 1 billion driving miles, capturing traffic violations that are otherwise difficult to detect.


The proliferation of driver management software has significantly improved compliance across fleets that have chosen to use them. Going by recent statistics, Driveri has improved stop-sign compliance by 55 percent across its customer base. Some fleets within this base have shown a more than 90 percent improvement where past trigger-based technologies have failed.

AI improves compliance primarily through analytics. By collecting and analyzing traffic data, fleets can work out new solutions to any compliance issues they may face. In addition to analyzing this data, AI can track driver performance concerning violations. This can be used as a basis for rewarding or training drivers in an effective and driver-friendly way.


The most notable areas of compliance improved by AI are:


Traffic violations including U-turn violation, lane violation, wrong left turning, tailgating and speeding, are leading causes of accidents on US roads. According to a report by the National Highway Traffic Safety Administration (NHTSA), speeding killed more than 9,000 people in 2018.

In 2016, more than 1 million speeding tickets were issued in California. Other states have closely related statistics for other violations as well. At this frequency, traffic compliance is a tough problem to deal with, especially because of the volume of vehicles that travel these roads daily.

Although drafting a policy that calls for strict traffic law compliance is a necessary first step, it can only do so much. Keeping track of how many drivers in a fleet have run stop signs without being caught is almost impossible without the use of AI.

AI streamlines this process, makes it sustainable, and works seamlessly regardless of onboarding processes for new drivers.

Using AI, Driveri captures every minute of the driving cycle to keep an accurate account of road events as well as intuitively analyze the data collected. This allows fleets to measure how compliant they are with traffic laws.

Across the Driveri platform’s 10 million most recent stop sign observations, there is a clear 61 percent improvement for non-stop events and 51 percent improvement for rolling stops.


According to a report by NHTSA, distracted driving accounted for more than 3,000 deaths in 2017. Distracted driving is associated with a range of behaviors such as using cell phones, eating, sleeping, fiddling with objects such as the stereo and even driving while intoxicated.

Unfortunately, managers cannot physically watch drivers or correct these behaviors in real-time. The use of cameras to detect distracted driving seems like the most obvious solution, but AI provides a better way to do this.

Ordinary cameras can record what goes on within the vehicle for playback later; however, the captured video has to be analyzed by a human. This may work for small fleets but quickly becomes unsustainable as the fleet scales.

In large fleets, managing such video playback can lead to hundreds of hours of analysis. Not only is this tedious, but it is also inefficient, prone to errors and only allows for a correction after the driver has exhibited distracted driving.

In some cases, this can be fatal. For example, cameras may record the moments leading up to an accident in which a driver showed signs of being drowsy. Unfortunately, cameras cannot do anything to prevent such an accident.

With AI, cameras collect video and analyze it in real-time to detect such behaviors. In the case of the drowsy driver, sophisticated AI can detect eye movement, yawning and other indicators of distracted driving and send a warning signal instantly. This lessens the likelihood of an accident occurring and integrates perfectly with any systems already set up to track driver performance.


Road conditions continuously change and some of these changes could affect how well a driver complies with traffic laws. For example, drivers may be following too closely in the rain, speeding in snowy conditions, or navigating through thick fog.

In some cases, drivers are not prepared to deal with such conditions. This is where AI comes in. AI has the ability to coach drivers in hazardous situations.

Having an onboard coach has several positive effects on a driver’s safety and performance, such as boosting confidence and improving decision-making.

AI coaches typically work by collecting data on the surroundings in real-time, analyzing that data and deciding on the best action to take. These calculations are carried out in a matter of seconds and are made possible by edge-computing as opposed to trigger-based technologies.


With a system such as Driveri handling fleet compliance, companies can achieve all of the following:

  • Reduce collisions and other road accidents by a significant margin
  • Ensure driver safety is a priority by helping drivers to mitigate risks at every turn
  • Save money due to fewer accident claims and lawsuits
  • Create a driver-friendly environment driven by incentives and rewards
  • Improve driver retention by boosting morale among drivers and creating an evidence-based environment
  • Accurately target areas where drivers require more training
  • Draft more specific company safety policies

Netradyne, the creator of Driveri, is a member of Protective’s Vendor Referral Network. To learn more about the Vendor Referral Network and how to find a safety-focused vendor to enhance your fleet, contact your territory manager or email

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