Generative AI has arrived, and it’s transforming virtually every business, including utility fleet management. Using the power of AI, utility fleets are finding new ways to improve safety, minimize downtime, lower emissions, and reduce fuel costs.
According to Bob Bradley, Associate Vice President of Data Science & AI Engineering at Geotab, a provider of connected vehicle and asset solutions, generative AI is changing how we interact with software and data. “Natural language is becoming the input and output of so many different systems,” says Bradley. “Fleet managers no longer need to write code or pull complex reports—they can simply talk to their data and get the insights they need through conversation.”
In 2024, Geotab introduced Ace, which allows fleet managers to converse with their data. “The reality is, you don’t have time to go through every row of data to make a decision,” says Bradley. “Machine learning algorithms look for patterns and find the needles in the haystack. Now, on top of that, we enable users to start asking questions, as if they had their own personal data assistant.”
Improving safety with AI cameras
There are significant rewards to improving fleet safety, including lower insurance costs, reduced downtime, and improved driver productivity. AI technology is growing in importance as a tool to enhance fleet safety. Take blind spots, for example.
Detecting pedestrians in blind spots with AI cameras
The Federal Highway Administration (FHWA) reports 840,000 blind spot-related incidents in the U.S. annually, resulting in over 300 deaths and tens of thousands of injuries. According to Corey Heniser, CEO of Brigade Electronics, the national average cost per blind spot accident for commercial fleets has been estimated at between $40,000 and $120,000 per incident (non-injury/non-fatality). Should the accident involve an injury or fatality, the cost could total millions of dollars.
Brigade’s Backeye®360 camera was originally designed as a surround vision system. Heniser says the new generation, Backeye®360 AI, utilizes AI to recognize when a pedestrian is in the blind spot and provides an audible alert, along with a visual box around the person on the monitor. The box changes color based on how close the pedestrian is to the blind spot. The addition of AI grabs the driver’s attention and provides the critical extra seconds needed to prevent an injury.
Research by the Highway Loss Data Institute (HLDI) found that blind spot detection lowers rates of insurance claims covering injuries and damage to other vehicles.
Detecting cyclists with radar and AI
Brigade’s Radar Predict technology also uses AI to determine if a cyclist is nearby and assesses the potential for an imminent collision. According to Heniser, traditional radar without AI would alert the driver to every vehicle. “By using AI, Brigade eliminates nuisance alerts by only providing audible warnings when there’s a potential collision,” he says.
Detecting distracted and fatigued drivers with AI-cameras
Utility fleet drivers often face long and irregular hours, especially when responding to repair infrastructure after natural disasters. In an era of nuclear verdicts, where large punitive damages are assessed, fleet managers are realizing they need to take a more proactive role in monitoring fatigue and distracted driving to reduce risk.
Positioned inside the vehicle, Brigade’s AI Driver Distraction Camera continuously monitors the driver's face, eyes, and head movements. Built-in AI analyzes the driver to detect signs of distraction or fatigue and provides audible alerts.
“Driver buy-in is the biggest obstacle to driver-facing cameras,” says Heniser. “It’s important that drivers understand that the AI Driver Distraction Camera is a tool for their safety.”
Brigade Electronics has taken privacy concerns seriously when designing its latest system, offering non-recording options to protect driver privacy, with alerts and snapshots only sent when a behavioral change is detected.
Managing collision risk with AI
Geotab is helping utility fleet managers turn millions of data points into predictive risk models that can be used to coach drivers and ensure they are working in the safest environments. Through their own Geotab GO devices or integration with OEM sensors, data is collected on what Bradley calls “harsh driving events.” An accelerometer in the GO devices measures gravitational force during events like harsh braking, acceleration, or cornering, while AI-powered dash cameras detect when drivers are following too closely and provide near real-time warnings. The footage also gives fleet managers a detailed view of collisions, allowing for better coaching and risk assessment.
According to Bradley, predictive risk models can be based on driving behaviors as well as the environment around the driver, such as weather, traffic density, or road conditions.
“We’re using AI and machine learning to bring this information together, add context, and build a predictive score so that experts can then manage and coach accordingly,” says Bradley. One of the benefits of using Geotab is that its big data modeling allows its customers to compare fleet vehicles to others of similar size, composition, geography, and driving patterns from across the entire Geotab ecosystem. “There is a lot of power in taking anonymized and aggregate data to build models,” says Bradley. “Most of our customers are looking to us to build those models that can help with their specific use case, backed by anonymized data from a 4.7 million-vehicle fleet.”
Managing predictive maintenance and electric vehicle suitability with AI
AI can also help fleet managers prevent costly breakdowns with timely maintenance and identify potential issues before they occur. For example, battery information from vehicles provides a mechanism to build models that help predict when batteries may fail.
Similarly, data captured on electric vehicles helps fleet managers understand driving patterns and other important factors when considering a transition to electric vehicles. “With those inputs, we have developed an EV suitability assessment that helps fleets identify vehicle options and calculate ROI, enabling fleet managers to identify which vehicles within their fleet make the most sense to transition to electric .”
Saving fuel and improving efficiency with AI route-planning
Fleet route optimization uses AI and near real-time data to create the most efficient routes. Route optimization GPS tracking provides precise location data, while telematics systems gather information on vehicle speed, fuel consumption, and engine performance. Data is then fed into routing software that considers factors such as traffic patterns, road conditions, and weather forecasts to determine the most efficient route for each driver.
According to Geotab, their Routing and Optimization Software increases the number of jobs that can be completed daily and reduces mileage by approximately 15 to 30%, helping to reduce fuel costs. For fleets with electric vehicles (EVs), driving more efficient routes can help preserve range, simplifying the use and management of an electric fleet.
Considerations when adopting AI for fleet management
While there are clear benefits to utilizing AI for fleet management, utilities also need to be wary of potential challenges, including data quality and data security. Utilities should question what practices are in place to identify inaccurate data.
“We leverage tools like AI and machine learning to identify outliers in the ecosystem, track down what may be happening, and try to be as proactive as possible with those pieces of data,” says Bradley. “It’s critical when looking for a telematics partner that the fleet chooses partners with deep experience in fleet management and a proven security track record.”
It’s important to ensure data security because a successful cyberattack could result in the theft of valuable intellectual property or financial information, leading to financial losses and damage to reputation. To mitigate risk, look for strong access controls, encryption, and intrusion detection to prevent attacks on vehicle systems.
Equally important is that your data integrates into your fleet management platform. Telematics and vision companies like Geotab and Brigade support the integration of their technologies to enhance the value of AI for fleet managers.
We’re just beginning to see the transformative impact of AI on fleet management, but it won’t be long before utilities can experience the results for themselves.
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