What does AI need to know to drive: Testing relevance of knowledge
Dominik Grundt, Astrid Rakow, Philipp Borchers, Eike Möhlmann · Science of Computer Programming · 2025
Artificial Intelligence (AI) plays an important role in managing the complexity of automated driving. Nonetheless, training and ensuring the safety of AI is challenging. The safe generalization from a known to an unknown situation remains an unsolved problem. Infusing knowledge into AI driving functions seems a promising approach to address generalization, development costs, and training efficiency. We reason that ascertaining the relevance of infused knowledge provides a strong indication of the correct execution of previous development phases of knowledge infusion. As a causal reason for AI performance, relevant knowledge is important for explaining AI behavior. This paper defines a novel notion of relevant knowledge in knowledge-infused AI and for requirements satisfaction in traffic scenarios. We present a scenario-based testing procedure that not only checks whether a knowledge-infused AI model satisfies a given requirement R but also provides statements on the relevance of infused knowledge. Finally, we describe a systematic method for generating abstract knowledge scenarios to enable an efficient application of our relevance testing procedure.