How Artificial Intelligence Impacts Fleet Management
Artificial intelligence and autonomous vehicles
An obvious application of artificial intelligence is in autonomous vehicles. Completely autonomous vehicles – with level 5 autonomy – will rely heavily on artificial intelligence to interpret the environment and react accordingly, though this technological advance is still some way off. Lower levels of autonomy for vehicles are already available in the automotive industry today: cruise control, autopilot modes, tracking of lane departure and other features to improve driver safety. And while early examples of autonomous cars and autonomous driving technology are promising, there is a long way to go before entire fleets are comprised of solely autonomous vehicles. For fleets, experiments in platooning and the successful completion of longer distance autonomous trips by semi trucks have shown how AI has the power to change how fleet vehicles operate more safely and cost effectively.
How is AI integrated with fleet management today?
For years, fleets have been able to track mobile assets in near real time using telematics. But the sheer volume of data collected from on-vehicle sensors and the wider internet of things available today calls for the integration of much smarter management systems to help keep pace. This deluge of data is set to increase as new technologies like 5G enable ever bigger streams of data to help inform fleet management systems. AI can be used behind the scenes to achieve more sophisticated management of this modern tech.
And the timing is ideal, given that recent Verizon Connect research indicates 79% of senior operational decision makers believe their organizations need a better way of managing mobile workforce operations, and the majority admit there are difficulties when it comes to managing operations (65%) and fleets/assets (62%).1 AI can positively influence these issues and many more, while providing the technology to help fleets remain competitive in the face of future industry shifts.
Yet, it’s only recently that fleet managers have gained the ability to apply AI-backed learnings across the organization to fundamentally change how a fleet operates and communicates. Thanks to machine learning, the component of AI that enables a system to “learn” and continually refine its interpretation of big data sets, fleets can improve the accuracy of telematics-derived data related to driving behavior, asset tracking, utilization and overall operations to improve safety, productivity and cost-efficiency.