AI and IoT, often referred to as AIoT, can benefit a variety of sectors. One of them is fleet management, where AIoT can help operators achieve their various objectives. This article series looks at how.
AI and IoT, often referred to as AIoT, can benefit a variety of sectors. One of them is
fleet management, where AIoT can help operators achieve their various objectives. This article series looks at how.
AI and IoT are companion technologies; one cannot go without the other. IoT devices gather data of various kinds, while AI processes and makes sense of these data. Together, AIoT benefits a variety of industries, from retail to transportation.
Fleet management challenges
One vertical that can benefit from AIoT is fleet management, where operators are faced with a range of challenges and difficulties. “Some of the major difficulties in fleet management today include supply chain disruptions, increased insurance premiums, high fuel costs, and more,” said Guang Sun, Lead Data Intelligence and BI Architect at Cetaris. “Fleet managers also face challenges such as avoiding information overload, integrating fleet data into existing software systems, making sure all assets are fully utilized, fixing small problems fast, managing a geographically dispersed team and finding specific fleet information quickly.”
“Fleet owners are looking at two big challenges. One is managing costs and efficiency related to the fleets they have now, and another is making the transition from carbon (gas powered) to mixed energy (gas and electric powered),” said Mayank Sharma, Head of Global Product Management and UX at Teletrac Navman.
“However,” he added, “the costs associated with the transition, along with the types of metrics for each fuel source, are different. For EVs, fleet owners are going to want to track: range (how long would it last), recharge time, and number of cycles of battery recharge, among others. Fleet owners need to be able to bring all the information and data around fleet performance into a single pane of glass.”
How AIoT can help
This is where AIoT can come in handy for fleet managers. IoT devices can gather a range of fleet-related data, for example truck location, fuel usage and
driver behavior, which can then be processed and analyzed by AI to give operators real-time visibility and insights.
“AI can help the fleet Industry solve its most persistent problems by decreasing costs associated with labor and parts, decreasing unplanned maintenance and repair expenses, increasing revenue generation and productivity, and improving safety,” Sun said. “AI-based recommendations can help skilled fleet workers excel by providing them with needed information and recommendations for action.”
Sun added: “AI can also help with real-time fleet optimization by giving drivers real-time weather, traffic and road condition data and identifying the fastest route to their destination. AI-based internet of things (IoT), data analytics and predictive maintenance are transforming fleet vehicle repair by anticipating an engine problem and reporting it before the driver notices that anything is amiss.”
“One of the best use cases for AI and telematics together in fleet management is managing energy costs,” Sharma said. “Using AI, fleet owners can predict energy consumption. Sensors can pinpoint problems such as vehicles that are consuming more. Based on this performance data, fleet owners can predict fuel utilization and costs for the next quarter. They can also make changes – like reducing idling – to lower them. Much of this data is being generated by IoT devices that are constantly reading the data from the vehicle and the engine.”
We’ll take a further look at specific AIoT use cases in fleet management in an upcoming article.
Common IoT devices in fleet management
AIoT cannot work without IoT devices that gather critical data. So what are some of the IoT devices in fleet management? “There are a couple of devices that are becoming more common. One is your general telematics location and engine data. The second are IoT sensors that are connected to various parts of the vehicle and also to the cargo. IOT dash cameras–on the windshield–captures footage from outside and inside the vehicle and can deliver a 360-degree view outside the vehicle itself. Data from the camera (video footage and images) provide a view looking outside the vehicle and what’s happening with the driver and on both sides,” Sharma said.
Once the data is gathered, the AI processes it either on the edge or in the cloud. For the latter, a stable and secure transmission method is required. In this regard, cellular technologies like
5G is an option.
“The existing solution in the market is to use the low latency of 5G. Using AI and GPU acceleration on AWS Wavelength or Azure Edge Zone, vehicle OEMs can offload onboard vehicle processors to the cloud when feasible. This approach allows traffic between 5G devices and content or application servers hosted in Wavelength zones to bypass the internet, resulting in reduced variability and content loss,” Sun said.