Considerations of Artificial Intelligence Safety Engineering for Unmanned Aircraft

Abstract

Unmanned aircraft systems promise to be useful for a multitude of applications such as cargo transport and disaster recovery. The research on increased autonomous decision-making capabilities is therefore rapidly growing and advancing. However, the safe use, certification, and airspace integration for unmanned aircraft in a broad fashion is still unclear. Standards for development and verification of manned aircraft are either only partially applicable or resulting safety and verification efforts are unrealistic in practice due to the higher level of autonomy required by unmanned aircraft. Machine learning techniques are hard to interpret for a human and their outcome is strongly dependent on the training data. This work presents the current certification practices in unmanned aviation in the context of autonomy and artificial intelligence. Specifically, the recently introduced categories of unmanned aircraft systems and the specific operation risk assessment are described, which provide means for flight permission not solely focusing on the aircraft but also incorporating the target operation. Exemplary, we show how the specific operation risk assessment might be used as an enabler for hard-to-certify techniques by taking the operation into account during system design.