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

21 resultaten, getoond 6 t/m 10

Fair machine learning

Informatietype:
Article

Fair machine learning is relevant to all kinds of discrimination and bias arising from the use of biased data. Read more!

Deep vision

Informatietype:
Article

Through Deep Vision, we’re developing AI algorithms to make automatic image analysis possible. Learn more!

Subsidence monitoring

Informatietype:
Article

The ground in the Netherlands is sinking. TNO is developing an AI model that will show which human activities contribute most to subsidence.

Predictive AI will soon make preventive healthcare possible

Informatietype:
Article

Predicting, based on health data, how likely it is that someone will get a particular disease or disorder. AI makes this possible in a safe way.

Fair decision making in the job market

Informatietype:
Article

Together with experts from the UWV, CBS and the Bureau for Economic Policy Analysis, TNO has shown in 2020 that AI can contribute to fair matchmaking.