Str(AI)ghtforward fleet management?

Artificial Intelligence (AI) has infiltrated nearly every industry … but this doesn’t mean that machines will take over the world. AI will just lend a helping hand – especially in the fleet management sector.

Netradyne, an industry-leading AI and edge computing provider that focuses on fleet safety, hits the nail on the head in one of its blogs: “AI may seem like science fiction, but it has already made its way into the things we use every day without us even realising it. Voice-controlled personal digital assistants, navigation apps, facial recognition, and other predictive capabilities are some of the forms of AI in our daily lives.”

The company, located in the US and India, provides fleets of all sizes and vehicle types with an advanced video safety camera, fleet performance analytics tracking, and driver awareness tools to help reduce risky driving behaviour and reward safe driving decision-making.

It has been added to this year’s The AI 50 list – produced by Forbes and Sequoia Capital, a US venture capital firm that recognises privately held companies in North America that are making the most interesting and impactful uses of AI.

In the blog, AI in Fleet Management – Why, How, and What to Look For, Netradyne points out that fleet management businesses have begun to use constantly evolving AI to prioritise resources, collect and analyse data, identify risky driving behaviours, identify areas of cost containment, and enforce compliance.

“Fleet managers are realising that AI is not meant to replace them, but rather assist them in making their role more productive and streamlined.”

Netradyne highlights some specific ways that AI can help fleet businesses manage their operations.

Improved driver and vehicle safety

AI systems can be trained to detect yawning and blinking frequencies, head turns, and signs of risky behaviour, for example, and broadcast them to managers in real time, while immediately communicating to the driver through audio notifications.

“Netradyne’s AI platform can identify when a driver has picked up a cell phone, for example, and remind the driver to put it down within 11 seconds,” explains Adam Kahn, president of the commercial fleet team at Netradyne. “In 11 seconds, our system has recognised a dangerous manoeuvre and invoked change.”

It can also make smart predictions about the weather, changing road conditions and gather data from other vehicles and road signs.

Reduced vehicle downtime

Current trucks have several electronic components and sensors that collect a lot of data, such as engine diagnostics ODB2 and CAN bus data, fuel usage, idle times, location, vehicle utilisation, and driving hours. AI can use this data to gain insights and make predictions.

It can also be combined with a camera system to detect worn tyres or missing underbody parts –and the process is much faster than a manual inspection.

Enhanced decision making

Fleet managers can use AI to determine relationships among different types of data to understand certain outcomes. Studying the performance of a driver over time can be used to arrive at a score for the driver and determine areas where the driver is prone to exhibit risky behaviour. For example, the AI model might determine that the driver tends to turn at a high speed while making a left turn.

AI can also be used to study the fuel usage in vehicles by looking at relationships among data collected and determine exactly if the problem lies with the driver, vehicle, or road conditions. Once the cause is known, corrective actions can be taken to improve fuel economy and safety.

And one of the wonders of AI is that it can offer corrective measures on its own, without requiring an active review by a fleet manager. This can include virtual coaching or a list of subset behaviours that a driver has to review or focus on.

How does this differ from normal fleet management?

It might seem that AI-enabled fleet management doesn’t offer anything new … but telematics platforms of previous generations – designed to eliminate paper-based reporting and processes – required users to spend an excessive amount of time analysing digital reports to identify opportunities for improvement.

AI can lend a helping hand and do it on its own (without taking over the world).