Keeping driverless navigation in the right lane with AI

Artificial Intelligence (AI) is what drives automated vehicles. Autonomous cars, in particular, are the future of transportation and need to adopt human-like reasoning when it comes to navigation. They need to constantly be absorbing information such as road condition and live traffic updates in order to ensure they can provide safe and efficient journeys for their passengers. In today’s digital world, AI is utilised heavily for accurate road navigation as well as vehicle operation

When in full swing, AI takes the most demanding of drivers from A to B with the best directions no matter how complex the journey may be. Deep learning models create specific, powerful maps that are able to process a huge volume of images that no human cartographer could possibly manage.

Unfortunately, piloting these solutions is a problem faced by car manufacturers today. When the control over navigation is taken out of the driver’s hands, there is a need to ensure that the data the AI is working with is up to speed. But how do manufacturers collect the huge amount of data needed, and how can they ensure this is kept up-to-date and accurate? With these considerations in mind, car makers must capitalise new modelling techniques and adopt a crowdsourcing approach to fuel AI through data collection.

Shifting ‘AI gears’ with data

By leveraging data and the Internet of Things the majority of modern 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. 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. 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.

Using the community for digital mapping

Keeping digital maps accurate and up-to-date is essential, and to do so community engagement becomes increasingly important. Car manufacturers now need 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.

As technology continues to drive the automotive industry further into the future, the reality of autonomous driving becomes closer. Soon, navigation systems fuelled by AI will become the norm in our vehicles. In order to ensure their success, manufacturers will need to ensure the data used to inform these systems is as detailed and accurate as possible. Data must be collected from a diverse range of trusted sources to unlock the entire potential of digital mapping. As data is increasingly sourced through new models and crowdsourcing reliant functions, the future of AI assisted navigation will become more positive than ever.