Spatiotemporal Ground Risk Mapping for Uncrewed Aircraft Systems Operations

Abstract

In this paper we propose the use of spatiotemporal population density data in the analysis of ground risk posed by uncrewed aircraft system (UAS) operations. The spatiotemporal population density maps are generated through the combination of authoritative data sources, open source geospatial databases, and past works to dynamically classify proportions of a population to their expected daily activities based upon a given time. This adds a further dimension to analysis allowing evaluation and optimization of ground risk, both spatially and temporally. This approach is used to analyze the ground risk posed under ballistic and gliding descents of a parameterized UAS along a case study path. An open source tool is implemented as part of this work to aid the decision making of operators and promote safer UAS operations.

Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation

“…This approach is complementary to the quantification of ground risk posed by UAS [22] and the use of both methods enables a holistic, objective, and quantified assessment of the risks posed by UAS operations. It is suggested that both the air and ground risk components be normalised to estimate the number of fatalities per single sortie of an operation and combined to a single value representing the probability of a fatality occurring.…”

Investigating the Crash Protection Performance of a Medical Carrier Bag for Drone Transport

“…The analysis and quantification of the potential risks and consequences of a drone crash are complex. In [29], a bespoke software model was developed to predict the risk of a fatality of an individual on the ground due to a drone crash based on dynamic population densities, which allows drone flight paths to be planned to avoid areas of higher risk. However, this model does not include any additional risks that may be involved due to the specific case of a drone carrying DG cargo.…”