Artificial intelligence (AI) has advanced to where it’s become a transformative technology for fleet management. One might consider AI a valuable assistant fleet manager whose role is to scour data looking for ways to improve fleet safety, uptime, and productivity in addition to reducing emissions. Artificial intelligence uses technology to mimic cognitive functions, such as seeing, understanding, and responding to language, analyzing data, making recommendations, and more. Machine learning is one application of AI that enables machines to extract knowledge from data and learn from it autonomously.
Improving fleet safety with AI-powered collision avoidance systems
“Our vision sensor cameras are trained to watch the road like a human eye would, constantly scanning for obstacles and potential hazards,” says Uri Tamir, General Manager of Mobileye North America, which manufactures AI-powered collision-avoidance systems powered by technology used in over 125 million vehicles on the road today. “Unlike the human eye, though, the technology never becomes distracted or fatigued. For utility employees driving long hours to the next worksite, it’s a second set of eyes keeping them safe.”
In a study done by the Insurance Institute for Highway Safety (IIHS) using Mobileye data, drivers triggered fewer collision alerts after driving with the system for some time, suggesting they became more aware of their behavior and drove more safely, reducing the risk of collision. Mobileye collision avoidance systems can be retrofitted to nearly any vehicle and feature alerts for forward collision warnings, lane departures, following time monitoring, speed limit indicators, and pedestrian collision warnings. The system can even be expanded to include blind spot detection for vulnerable road users such as cyclists and pedestrians.
Corey Heniser, CEO of Brigade Electronics, says their company has seen growing interest among utilities for its AI-powered camera. “AI provides a more active alert than a camera,” says Heniser. Using artificial intelligence, Brigade’s active blind spot detection recognizes human forms within the pre-defined detection zone and reliably warns the driver visually and/or audibly before a possible collision occurs. Image processing is built-in to the camera, so no other hardware is required.
Connected vehicle and asset management provider GEOTAB leverages AI to look at comparable fleets based on their vehicle mix and driving patterns. By comparing apples to apples, it enables fleets to see and quantify where their opportunities are for improvement. “Now we can look at other fleets like yours and come up with a probability of a collision,” says Mike Branch, Vice President of Data and Analytics. “This allows us to be more prescriptive.”
Geotab also uses AI to enhance the quality of collision data by pulling in contextual elements into the determination of a collision, weeding out false positives. Research shows that in 2022, Geotab-Connected Commercial Vehicles saw a global average improvement of 17.45% in millions of miles driven prior to a collision (from 0.91M miles in 2021 to 1.06M miles in 2022.
Improving uptime with AI-driven predictive maintenance
Geotab is also using AI to help fleet managers predict when certain systems will fail. Using hundreds of thousands of telematics data points from vehicles of all models and classes, the system identifies patterns that cause past failures. Real-time alerts inform fleet managers of a potential failure before it actually happens, allowing them to address issues when they are less expensive problems.
“We would not be able to make these predictions without all the right data,” says Branch. “We’re running 55 billion data points from over 3.7 million commercial vehicles.”.
Improving fuel economy with AI
The same way AI can help predict equipment failures, it can also identify vehicles that are consuming more fuel than average, whether it’s due to idling, underinflated tires, low engine oil, or another issue. Proper vehicle maintenance can improve fuel economy by as much as 5% to 10%.
What to look for in AI-powered solutions
According to Tamir, AI has undergone a revolution with the development of deep neural networks to replace the more traditional machine learning of the past. DNNs have significant advantages in extracting features at different levels of abstraction and thus they can identify more complex patterns. Mobileye technology leverages deep neural networks in their fleet safety technology to identify a wider range of potential road hazards more accurately and from a further distance. Geotab is now working with generative AI, which allows fleet managers to pose questions about their fleet performance.
When looking at AI-powered solutions to manage your utility fleet, one element that really matters is the quality of data.
“Your insight is only as good as the quality of data coming in,” says Branch. He also advises companies to evaluate how well the data can integrate with other aspects of their operation. “You want it to be easy to get those insights where you need them.”
You also need to be concerned about data privacy. “Make sure that you’re acutely aware of how your data is being processed,” says Branch.
Cost/benefit analysis of AI-powered solutions
Investments that promise a positive impact on safety are justified when you consider the high cost of collisions and the liability if someone is injured or killed by a fleet vehicle. Similarly, rising fuel and vehicle prices make fleet management investments easier to justify. Review success stories from Mobileye and Geotab to learn more about what you can expect from AI-powered fleet management solutions.
Mobileye, Brigade Electronics, and BEOTAB will all be exhibiting at The Utility Expo 2023: the largest and fastest growing trade show in the utility industry. For more information on the more than 860 equipment manufacturers and service providers that will be exhibiting at the show, check out our Exhibitor Directory.
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