SafeCert: Towards Automated Safety-Case Generation for Risk Assessment in Small Uncrewed Aerial Vehicles

The increasing use of small Uncrewed Aircraft Systems (sUAS) in managed and often contested airspace has raised public safety concerns, creating a need to proactively provide risk assessment ahead of flight. Such assessments require clear reasoning and supporting evidence to demonstrate the safety of the intended flight. In this paper, we first present an initial analysis of incident reports from three different international databases. We find that for the US-based database that user error is the predominant cause of incidents, but for the UK-based database, mechanical failure is more frequently reported. We then present SafeCert, a prototype software tool developed to assist sUAS operators by automatically analyzing and visualizing the safety-related evidence associated with an upcoming flight. SafeCert takes as input a feature model that captures operational variabilities, such as drone model and environmental conditions, and a parameterized safety argument representing acceptable justifications for flight safety. Next, based on the operator’s responses to a dynamically generated set of flight-specific questions, SafeCert compares these parameters against the safety argument and outputs a color-coded visualization of a structured safety argument diagram. We believe our database analysis can be leveraged to refine the questions, feature model, and safety arguments in SafeCert.