Looking ahead to a future where autonomous driving is expected to play an enormous role in our everyday lives, when it comes down to who is taking the wheel it’ll be Artificial Intelligence (AI) which we’ll need to thank.
AI is not only vital in ensuring successful and efficient navigation, but it’s a crucial element in ensuring the journey from A to B is as safe and comfortable as possible.
The biggest benefit of AI is its ability to boost efficiency and complete complex tasks that cannot be easily managed by humans. When it comes to navigation, this translates to evaluating real-time conditions with optimum route guidance that helps the driver avoid traffic, amongst other road hazards.
The implementation of AI into cars, however, is no easy task. When the control over navigation is taken out of the driver’s hands, there’s a need to ensure that the data the AI is working with is up to code. Fortunately, with new modelling techniques and a crowdsourced approach to data collection, car makers can provide the fuel to enable AI-assisted driving.
The stronger the data, the stronger the AI
Most modern car navigation systems can leverage data and the Internet of Things to alert for 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. Yet, machine learning 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 recognise roads but also respond to them.
Increasingly, car and mapmakers also need to keep privacy and security in mind. Few data is as sensitive as customer location history, proving a challenge for those who rely on large-scale data to ensure the accuracy of their maps. Privacy-aware machine learning, made up of AI algorithms that learn from anonymised raw data, is the answer. Once trained, the models can be shared, allowing companies to continue to train and enhance the full pool of shared models with new ones.
Community engagement to power digital maps
To keep digital maps accurate and up-to-date, a key ingredient is community engagement. It’s essential for car manufacturers to develop – or seek out – navigation systems with a built-in community of people who are willing to help keep maps accurate. Whether it’s through an app or interface which allows people to contribute, drivers can share images of where reality doesn’t match what has been recorded, such as road closures or road signs. This then alerts mapmakers to check and fix the map as necessary.
The challenge of building this community, however, is consumer suspicion. As the world becomes increasingly more aware that services such as online mapping tools can often be ad-funded, customers are less likely to share their data. How can we trust these service providers to send us the quickest route when in-fact what they’re doing is sending us on a detour past one of their advertisers? That’s why car manufacturers need to seek out a navigation system that isn’t advertiser funded.
AI navigation systems are at the helm of our ever-evolving transport network, ensuring more accuracy and more responsiveness. However, manufacturers must ensure a solid foundation is present first. The most successful digital maps rely on accurate data from multiple trusted sources. In particular, abstraction through AI modelling and crowdsourced maps, will reduce the computing load on systems and ensure AI-assisted navigation continues to be safe and successful.
About the Author
Pierluigi Casale is Group Data Scientist at TomTom. Throughout his career, Pierluigi has worked in academia and industry to develop machine learning algorithms and data analysis pipelines for IoT applications. With the goal of improving human life through extracting actionable insights from unstructured messy data, Pierluigi drives all Data & AI initiatives at TomTom. https://www.tomtom.com/
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