Decision making in autonomous driving

Thema:
Artifical intelligence

Busy intersections are hard enough for people to cope with. So how do we ensure that self-driving cars can safely make their way around towns and cities in the future? TNO is currently experimenting with ‘hybrid AI’ as part of an EU collaborative project.

A constant stream of pedestrians and cyclists. The driver behind you banging impatiently on his horn. For car users, negotiating such chaotic traffic situations can be trying enough, but for self-driving cars, it is even more difficult. After all, there are always sensors that notice an approaching object.

Write the software by hand? forget that!

In other words, self-driving cars are a long way off being able to deal with the complicated traffic situations that exist in urban settings. In the meantime, though, more and more driver-related tasks are being automated in cars. That requires complex software – so complex that it is not possible to write it by hand.

An autonomous car that takes the right decisions

Working closely with NXP, Infineon, DAT. Mobility, AnyWi, and Eindhoven University of Technology, TNO is now part of an EU project dedicated to preparing self-driving cars for urban traffic. Together, we are attempting to demonstrate that applying AI algorithms will make decision making possible. This is an important point, as it could swiftly accelerate the development of a safe autonomous car.

No standard AI, but ‘hybrid’ AI

The AI algorithm works closely with a knowledge graph. This is an information system that AI requires in order to be able to properly assess dangerous traffic situations, and therefore make the right decisions. This ‘hybrid AI’ is a combination of data-learning AI and domain knowledge.

Motorways first, and then more difficult situations

A car fitted with radar, lidar, GPS, and cameras drove around for several days for the purpose of collecting data. TNO is now using the information that it collected to train the AI algorithm. At this first stage, the scope does not extend beyond situations on motorways. This because the conditions on motorways are more clear-cut. The next step will be a good deal more complicated: this will involve the AI algorithm having to decide when a car is able to cross an intersection where there are cyclists. Safely, obviously. In other words, the self-driving car will have to constantly monitor the behaviour of cyclists and respond accordingly.

Handy in harbours

In due course, this type of AI algorithm could also have an important part to play in many other applications, in addition to self-driving cars. One such possible example is that of automated guided vehicles (AGVs) in ports. Another is a care robot, moving around in a hospital. This is another setting that would benefit from an effectively functioning vehicle.

Get inspired

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Educating AI

Informatietype:
Insight
27 September 2022

You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?

Innovation with AI

Informatietype:
Insight
27 September 2022

What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.

Innovating with innovation AI

Informatietype:
Insight
27 September 2022

How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?

David Deutsch on the development and application of AI

Informatietype:
Insight
27 September 2022

Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?

Georgette Fijneman on the promise of AI for health insurers

Informatietype:
Insight
27 September 2022

Hanneke Molema, senior consultant healthy living at TNO, interviews Georgette Fijneman, CEO of health insurer Zilveren Kruis since 2017. Both look at the same topic, health, from a completely different perspective. What is the promise of AI for one of the Netherlands’ largest health insurers?