Evaluation of Runtime Monitoring for UAV Emergency Landing
This addresses safety certification for UAV operations in populated areas, but appears incremental as it builds on existing EL and monitoring research.
The paper tackled the problem of ensuring UAV safety during failures by proposing an Emergency Landing (EL) approach with runtime monitoring for learning-based components, and found it to be much safer than a default parachute strategy.
To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer.