Decision making in autonomous driving
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.
Christopher BrewsterFunctie:Senior scientist
Christopher Brewster is a Senior Scientist in the Data Science group and Professor of the Application of Emerging Tecnologies in the Institute of Data Science, Maastricht University. His research has focussed on the application of Semantic Technologies, Open and Linked Data, interoperability architectures and Data Governance, mostly to the food and agriculture domains.
Daniël WormFunctie:Senior consultant
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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
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
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?
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