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The new concept for operation of drones, published by EASA in 2015, enables new ways to influence and possibly reduce the necessary safety targets of certain system components without reducing the overall safety of the unmanned aircraft system (UAS). Based on the safety assessment, the specific category enables new aircraft system architectures and mission designs. In this context, this paper analyzes runtime monitoring as a strategy to contain the UAS in its operational volume. To assure predefined properties in flight and thus assure the safety of the operation in progress with a high robustness, a formal methodology for safe operation monitoring is utilized. With this approach, this work targets to link the concept of safe operation monitoring with the upcoming regulations regarding the specific category and the specific operation risk assessment (SORA). One particular aspect of this safe operation monitoring is geofencing, the capability to contain a UAS in a previously restricted area. In the regulatory framework of a specific operation, risk assessment is required and so is the containment of the UAS in its operational volume. The functional and safety requirements for geofencing regarding their impact on the underlying specific operation risk assessment are discussed. To facilitate this discussion, a taxonomy of geofencing characteristics is derived based on a literature survey. Consequently, the geofencing requirements are assessed regarding their robustness and applicability for certification purposes. As a result, by monitoring the integrity of the system at runtime using geofencing as an example, it is investigated if the requirements and thus costs of development and certification process for the remaining components can be reduced.

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.