Highly automated vehicles are an integral part of everyday traffic and will be used even more in the future with further autonomy. The world is dynamically evolving with an "open-world" nature, creating new domains for highly/fully automated vehicles to learn. The environmental changes and developments are not only important for in time and location aspects, but also the constant developments in the field of hardware, such as sensors, software, and development states, pose great challenges. Up until now, artificial intelligence (AI) modules in autonomous driving applications are only scalable to a limited extent. The reason lies in the training strategies applied, in which algorithms have to be trained for each new domain. This results in enormous development costs.
This workshop addresses the need for new training methods for AI systems and researches. Disruptive methods of "effective" machine learning can enable a more efficient and unrestricted use of AI and thus “scaling of autonomy” in new domains. With this approach, automated vehicles can access new markets faster and respond agile to new demands.