The convergence of 5G, high precision data delivery and AI

Arn Hayden Business development manager, Trimble Positioning Services

Brandon Sights Engineering director, Trimble Autonomy

When we sit back and look at market conditions plaguing the economy today, we see two key issues: workforce shortages and supply chain demands. New technologies are constantly being deployed to address these issues, but the most impactful and successful ones are yet to come. Workforce shortages are driving the need for more autonomous solutions, and supply chain is driving the need for better tracking, autonomous equipment, and a more efficient deployment of resources.

While autonomous solutions are not new, they need to be expanded and improved to truly address these issues. This can be done by expanding autonomy into a wider spectrum of equipment beyond the largest mining vehicles and farming tractors of today. Along with this expansion, there is a greater need for situational awareness around these vehicles and equipment.

To advance autonomous solutions, we look to the convergence of greater data capacity and increased worldwide network coverage of 5G, high precision location data over cellular, and evolving artificial intelligence (AI) models in 2023. This technology combination will push greater efficiencies and accuracies in the real timebased applications needed to address both workforce shortages and the supply chain.

Tying 5G and AI Together

The increasing availability of high data capacity 5G, evolving AI models, and highly precise location data can now more easily address the future demands of the rapidly advancing capabilities of autonomous vehicles and equipment. But this technology combination can also enhance the interactions between vehicles and people. The 5G networks (and private 5G – an alternative to Wi-Fi, LTE and public 5G) being deployed today bring to the market much greater speeds, capacity, the number of connections and reduced latency that will be critical to transport data for AI models and autonomy. AI models will be called upon to orchestrate these autonomous interactions, but also to collect and utilize data on traditional vehicles as well. Models are only as good as the data they ingest, and precise location data is an important part of any AI model related to mobility and autonomy. Trimble’s RTX technology is designed to deliver this highly accurate and precise location data across the globe, making it well-suited to address autonomous applications.

Chipset manufacturers are now developing platforms with Trimble’s precise location technology that are available to mass markets in the newest smartphones and robust Internet of Things (IoT) devices. The availability of this capability will proliferate more, higher accuracy location data and applications than ever before. It will not only create better data, but also far more data points for AI models to digest, providing safer and more efficient operations. AI models that merge data analytics, sensor data, precise location data and video will improve personal safety, improve operational efficiency, lower insurance costs, create better user experiences, alert drivers of poor driving behavior, reduce delivery costs and may even improve crime fighting capabilities.

AI Today and in the Future

In the past five years, the adoption of AI has more than doubled, according to a recent study from McKinsey. Today, AI is being used primarily in the agriculture, construction, transportation, and mining industries to plan fleet operations, route trucks and machines, generate designs, monitor worker efficiency, or optimize the use of inputs automatically such as fertilizer or seeds. With the benefits of 5G combined with highly precise location data, AI solutions will be used more and more to alleviate many common workforce challenges, whether it’s making unpleasant tasks better or helping optimize around staffing shortages or freeing up time for workers to focus on tasks that cannot be handled by AI. On the supply chain side, AI models will be able to get data from anywhere in the world and into the cloud to train new models, which is difficult to do over today’s cellular networks. Similarly, the local 5G mesh networks will allow for real time sharing of the as-worked data from each machine or worker, further enhancing accuracy and productivity.