Highly and fully automated vehicles are expected to become an integral part of traffic. However, they face a large variety of complex traffic situations in a continuously evolving environment. This leads to great challenges for the development of autonomous vehicles ready for serial production.
Apart from dealing with changes in software and hardware, automated vehicles must learn to cope with constantly changing situations, and therefore with different so-called domain shifts.
The KI Delta Learning project investigates methods of effective machine learning that enable a more efficient and unlimited use of artificial intelligence (AI) and, hence, the implementation of automated driving in the "open world". Thanks to the methods developed for this purpose, automated vehicles can be faster deployed in new markets and react more agilely to new requirements.