A conceptual third party risk model for personal and unmanned aerial vehicles

Abstract

A conceptual third party risk (TPR) model is developed, implemented, and tested to demonstrate the capability of determining the risk to the population in a metropolitan city exposed to personal aerial vehicles (PAVs) and unmanned aerial vehicles (UAVs) traffic operations. The conceptual model that is primarily based on the methodology and experience of risk modelling for conventional aircraft is modified and the parameters are adjusted suitable for a determination of risk in the arrival, departure and cruise phase of flight for those new aircraft types. Without the availability of the empirical accident data of these aircraft, however, a validation of the conceptual model is not possible. For the purpose of comparison of different future operation concepts and different scenarios in a metropolitan area the model suffices the need. Two examples of operation concepts and traffic scenarios for PAV and UAV aircraft movements, respectively, are demonstrated. 978-1-4799-6009-5/15/$31.00 ©2015 IEEE 1301

Preliminary Damage Severity Evaluation of Ground Vehicles and Covered Walkways under Collision with a Small Unmanned Aerial Vehicle (sUAV)

“…8(a). In several research studies on UAV crash trajectory [21][22][23], it was found that the angle at which the UAV impacted the ground is not fixed, but a considerable part of them is approximately at 90°. Therefore, in the collision analysis, the collision angle is selected as 90°, as shown in Fig.…”

Real-time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

“…Following the development of the undesirable event model, the next step involves the collection and population of the data. As highlighted by Aalmoes et al, 6 given that unmanned systems are in their early development stages, operational data needed to populate third party casualty models are lacking. Consequently, the data necessary to drive numerous risk models were approximated through various sources such as numerical distributions, 6, 23 manned aircraft failure rates, 4,6,11 and/or military unmanned or manned operational data.…”

“…As highlighted by Aalmoes et al, 6 given that unmanned systems are in their early development stages, operational data needed to populate third party casualty models are lacking. Consequently, the data necessary to drive numerous risk models were approximated through various sources such as numerical distributions, 6, 23 manned aircraft failure rates, 4,6,11 and/or military unmanned or manned operational data. 10 The authors believe that, although such assumptions might be suitable for approximations or comparative studies of various future operational concepts, reliability data from manned aircraft operations do not provide an adequate baseline due to vast redundancy and configuration differences between manned and unmanned aircraft as well as the varying operational context (military vs. civilian aircraft).…”

“…The research concluded that vast majority of crashes occur close to the initial failure location. Similarly, Aalmoes et al 6 attempted to represent the potential impact area as a function of the aircraft weight, speed, direction and maximum glide distance. Recent research by Foster & Hartman 24 provided simulation predictions of multirotor trajectories during propulsion failures.…”

“…Within the same context, Lazatin 4 and Lum & Waggoner 5 estimated insurance liability for various UAS use cases based on fatalities due to ground and mid-air collisions by employing population density, population distribution, and sheltering factors. Similarly, Aalmoes et al 6 developed a conceptual third party risk model that estimates the potential impact area and the consequence area. Using a different approach, Clothier et al 7 assigned various UAS type categories based on kinetic energy and impact severity (injury or fatalities).…”