Businesses have come to realize that data is one of the most important corporate assets they can possess over the past decade. This realization is not surprising, given the IoT’s continued growth. Experts predict that there will be nearly 80 zettabytes worth of data generated by IoT devices connected to 41 billion devices by 2025. Companies have turned to location-based AI and other emerging technologies to extract the insights they need to move their business forward.
AI is a key technology that can be used to analyze location data and create more precise maps. There is a growing demand for advanced AI solutions, especially location-aware AI, due to increased competition between companies in the transportation, logistics, and automobile transportation (T&L), and public sectors. This is AI that can understand the properties and uses these insights to build applications and products.
AI-infused with location data is used to generate pattern recognition and place signatures from the data it gathers. It also serves as a key component in the generation of high-definition maps and realistic simulators to visualize this information. These intelligent visualizations enable next-generation mobility by understanding consumer movements and powering autonomous driving.
The AI value chain: then & now
Over the last few years, the AI value chain in the enterprise sector has changed dramatically. Previously, organizations were focused on machine learning (ML), but the focus has shifted to AI and ML technology to build solution model architectures and algorithms. However, in recent years, AI and ML technologies have been used to provide solutions that allow users to develop, compose, and scale data sets.
Recent standards in the enterprise sector have made it easier to use AI and ML to gain location intelligence. This has made it easier for sensors, satellites, or aerials to be used to create standard-definition maps. These same technologies have been used to create more precise HD maps. They are created by machines for machines and are now an integral part of autonomous driving.HD maps make it easier to combine multiple sources to identify features and patterns. They can also deal with real-time and static events to predict behavior and conditions.
An example of this is AI/ML-powered maps. The end-to-end process creates a self-healing map that continuously collects ‘lower-level and ’higher-level observations and aggregates map features. These technologies work together to adapt and evolve each map feature (e.g. signs, lanes, and pavements), and they are tailored for each geographical region. These maps are not able to capture the details associated with accurate data collection. This is why it is more crucial for location-aware AI technology to be implemented.
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Location-aware AI, in short, is designed to comprehend the relationships and properties of location information it receives. It also generates more advanced insights. This is possible by using real-time semantic relationships between physical objects. These can be used to create location graphs that are capable of providing more precise geospatial-temporal representations of the world.
Real-time data such as traffic, weather, and sensor data can be used to make better business decisions. Location graphs, when combined with location-aware AI allow professionals to create new data patterns and produce more precise samples from the data it collects. Location-aware AI is able to reveal key features and combine with other data in ways not possible with traditional AI approaches.
Application of location-aware AI within the transportation and logistics industry
The T&L industry faces many challenges, including how to optimize large-scale data. T&L vendors are looking for new ways to solve this problem due to the sheer volume of data that is being collected from vendors, consumers, and providers. Despite many solutions being developed over the years, location-aware AI is the most popular technology within the T&L sector.
In particular, reinforcement learning (RL), an AI-powered solution that addresses data optimization problems, has been very popular in the T&L sector. This technology allows professionals to build predictive models and simulations that can be used for business intelligence. It also provides simulation and sensitivity analysis capabilities. Essentially, companies can use RL technology for better business decisions regarding better fleet management and efficient distribution networks.
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Collaboration between the public and private sectors
Although location-aware AI has shown great potential in the T&L and automotive spaces as well as smart cities, no industry has yet fully benefited from this technology. It’s clear that AI/ML advancements are unlikely to be made behind closed doors. Instead, they will take place in open environments where innovation and collaboration can occur.
To encourage further adoption of location-aware AI technology, stronger collaboration between the public sector and the private sector is key. This would allow for more smart city initiatives related to public safety and enable vendors in the automotive and mobility sectors to develop more accurate and reliable AI-based location intelligence offerings.
If widely adopted, it is clear that location-aware AI can revolutionize the enterprise sector. Already, location-aware AI has proven its worth in providing more precise HD maps and more robust location intelligence for vendors across many industries. It is imperative that all professionals in the enterprise take a close look at location-aware AI solutions to help them find more reliable and accurate digital solutions for their business.
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