Fueling driverless navigation with AI

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Artificial Intelligence systems will be at the helm


Navigation AI

Navigation AI- Generally, Artificial Intelligence (AI) helps to make products and procedures more efficient, but it also handles intricate tasks that can’t be easily managed by humans. When navigation comes into play, AI can help to give drivers the very best directions, despite the complexity of the route.

Powerful, accurate maps can be created by deep learning models, able to process masses of images no human cartographer could ever manage.

AI is nothing without data

By leveraging data and the Internet of Things, the majority of car navigation systems are able to alert drivers of any travel disruptions and readjust the journey route accordingly. Yet few are complex enough to anticipate how the traffic situation will change during the travel time on any possible route. In the European road network alone, a hundred quadrillion routes are theoretically possible. Machine learning, therefore, allows for this through a process called dynamic routing, which helps AI navigation systems to actually predict how traffic will change and how the journey will be disrupted.

With dynamic routing, drivers and automated vehicles can drive with foresight. However, building the AI models needed isn’t the hard part. It’s data that makes the difference. For instance at TomTom, immense quantities of image data depicting street views are needed to create high-definition maps. To ensure the navigation system is responsive, there’s also a need for data on the same streets under a wide variety of environmental and weather conditions. The more data there is, the more accurate the maps will be.

An enormous amount of data is required to train AI models so that they truly represent reality. The job of photographing every stretch of road in every weather and lighting condition is obviously impossible. Abstracting the process through AI, however, can help us achieve such “impossible” but vital tasks. Through the use of novel generative algorithms, it’s possible to train AI to take one image and apply different conditions to it. For example, the AI could simulate the same street at night or during a blizzard. Thus, when an AI-enabled navigation system encounters atypical conditions on the road, it can adapt rather than lock up. This is a crucial step in helping AI not only to recognize roads but also respond to them.

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Article Credit: TechRadar