In- Time Safety Assurance Systems for Emerging Autonomous Flight Operations

Abstract

As aviation adopts new operational paradigms, new vehicle types, and new technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before they become hazards. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments wherein the interplay of hazards may not be known, and cannot be accounted for at design time. They also can help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected nondeterministic behaviors. The envisioned monitoring functions can observe these behaviors and apply model-based and data-driven methods to drive downstream assessment and mitigation functions, thereby providing a level of run-time assurance. This paper presents a preliminary conceptual design of such an in-time safety assurance system for highly-autonomous aircraft operating at low altitudes near and over populated areas. Research, development, and evaluation tests are initially aimed at public-use surveillance missions such as those needed for infrastructure inspection, facility management, emergency response, law enforcement, and/or security. A longer term goal is to support transportation missions such as medical specimen delivery and urban air mobility. Safety-critical risks initially addressed within the system concept were identified in previous work by NASA and others in industry. These include: flight outside of approved airspace; unsafe proximity to people or property; critical system failures including loss of link, loss or degraded positioning system performance, loss of power, and engine failure; loss-of-control due to envelope excursion or flight control system failure; and cyber-security related risks.