Development of methods for integrating knowledge into machine learning
AI-based processes are paving the way to fully automated driving. Up until now, the development of AI solutions has been purely driven by data. This datadriven approach requires enormous amounts of data for the training and validation of AI functions, with the collection and processing of this data being very resource-intensive and expensive. In addition to the dependence on extensive amounts of data, data-based AI processes have another weakness: they are still generally black-box models for which the decisionmaking process cannot be directly reconstructed.
In the research project KI Wissen, methods for integrating existing knowledge into the data-driven AI functions of autonomous vehicles are being developed and investigated. The goal of the project is to create a comprehensive ecosystem for the integration of knowledge into the training and safeguarding of AI functions. By combining conventional data-based AI methods with the knowledge- or rule-based methods developed in the project, the basis for training and validating of AI functions will be completely redefined: This basis now includes not only data, but information, i.e., data and knowledge. The development from data- to information-based AI carried out in the project addresses the central challenges towards autonomous driving: the generalization of AI to phenomena with small data bases, the increase of the stability of the trained AI to disturbances in the data, the data efficiency, the plausibility check and the validation of AI-supported functions as well as the increase of the functional quality.