About "KI Familie": Collaboration in Artificial Intelligence
Classic automotive questions are re-emerging with regard to AI. Know-how in the field of artificial intelligence (AI) and the safe use of this technology in modern vehicles will determine the leading role in the mobility markets of the future. The German automotive industry has risen to this challenge with the projects of the KI Familie. With their complexity regarding the subject matter and their connected structure, the projects build up comprehensive AI expertise for the entire range of automotive applications.
The KI Familie was initiated and developed in context of the VDA Leitinitiative for autonomous and connected driving. 80 leading partners from science and industry are involved in the projects. The projects of the KI Familie receive funding from the Federal Ministry for Economic Affairs and Energy.
AI based processes pave the way to fully automated driving. So far, the development of the AI processes has been purely data based. Enormous amounts of data are required for the training and validation of the AI functions, the collection and processing of which is very time consuming and expensive. In addition to being dependent on extensive amounts of data, data based AI processes have another weakness: they are still usually black box models, their decision making cannot be directly traced.
In the research project KI Wissen, methods for the integration of existing knowledge into the data driven AI functions of autonomous vehicles are developed and examined. The aim is to create a comprehensive ecosystem for integrating knowledge into training and securing AI functions. By combining conventional data based AI processes with the knowledge based methods developed in the project, the basis for training and validation of the AI functions is completely redefined: This now includes not only data, but information, i.e. data and knowledge. The development from data based to information based AI, carried out in the project, addresses the central challenges on the way to autonomous driving: the generalization of the AI to phenomena with a low data basis, the increase in the stability of the trained AI to disruptions in the data, the data efficiency, the plausibility check and the protection of AI supported functions as well as the increase in functional quality.