What does AI need to know to drive: Testing relevance of knowledge

Abstract

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.