In high-density environments, we find that loss of separation events increase linearly with the effective flight rate. And while preflight deconfliction techniques decreased the loss of separation rates, they also decreased the effective flight rate in a region. Even at the lowest volumes, we observed losses of separation occurring at rates about four orders of magnitude more frequently than occur in today’s airspace. Therefore, to achieve safe airspace usage, regardless of separation criteria, we need to apply a greater set of tools to managing flights, just like air traffic controllers do today. Relying only on simple preflight deconfliction rules and allowing flights to operate without having to use traditional airspace structures (e.g. altitude separation, one-way routes or charted arrival procedures) simply does not provide a path to safe airspace usage.

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This is the first public release of the draft of Altiscope’s quantitative open risk framework for unmanned aerial systems. It provides a direct path to implementation in present-day scenarios, as well as the conceptual groundwork to enable increasingly complex, dense and autonomous UAS operations informed by risk. The first chapters provide the justification for the framework and situate it in relation to other efforts to identify UAS risks. Next, this framework outlines a variety of high-level use cases so that various users can understand how they might use the framework. We provide conceptual details of several derivative models using the framework. So that users of this framework can gain a better understanding of how it might be applied and implemented, we provide detailed calculations and derivations for present-day small vehicle missions.

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This paper identifies urban air mobility (UAM)1 noise challenges and analyzes potential solutions for managing noise to ensure sustainable growth. We review existing scientific literature on unmanned aerial vehicle (UAS) noise impacts, including relevant literature on manned aviation noise impacts and define technical terms. We identify gaps in research and knowledge on UAS noise generation, mitigation and effects. To accommodate high-density UAS operations, we propose a balanced approach that encompasses many solutions and identifies potential trade-offs for flights over noise-sensitive areas. To manage urban air mobility noise, we recognize public perception challenges and discuss annoyance levels. Finally, we evaluate governing rules and highlight the importance of working with all stakeholders in defining standards for UAM.

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Numerous companies and researchers have demonstrated the ability of small unmanned aerial vehicles (UAVs) to swarm or flock together. While the largest and most eye-catching displays are intended more for entertainment, Altiscope believes the underlying principles will be a crucial tool for autonomously managing future airspace density and capacity constraints. Formation flight serves many purposes in military and general aviation today. Separately, visual separation techniques available to controllers in today’s airspace allow two aircraft that can see one another to fly much closer than prescribed separation minima otherwise would. Altiscope proposes a path toward establishing a set of UAV flocking behaviors based on both formation flight and visual separation that would take advantage of onboard sensor equipment and vehicle-to-vehicle (V2V) communications links. Taking an approach that is rooted in present-day policies and regulations should enable faster adoption of these procedures in an autonomous realm, compared with creating new regulations from scratch.

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Many studies and design discussions are concerned with “dense” or “high density” UAV operations — but there is no definition of what this term means. We propose two metrics that can be used to determine when the traffic in flight is dense. These metrics are based on an intuition that density matters when the vehicles in flight interact with each other. We find that the absolute number of vehicles in a volume is not, by itself, a good metric for determining dense operations, but that instead our proposed metrics reflect the effects of varying degrees of order in traffic flows. Analysis of the results suggests that traffic can become “dense” at low traffic volumes, including levels much lower than anticipated demand in urban areas.

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Altiscope is using fault trees to better understand the factors driving UAS loss of control accidents. Through extensive modeling and statistical analysis, we find that weather, electrical system and maintenance-related variables have the greatest influence on whether a UAV is likely to lose control and crash. In modeling flyaway events, we note that communications link degradation and compass errors are the most significant predictors of a loss of control. Additionally, there is a need for greater training and certification standards for any people involved in the operation — even in the case of a fully autonomous fleet — to reduce the risk of human error. And we conclude that detect-and-avoid commands need to provide sufficient lead time for the vehicle to be able to react and remain within its performance envelope.

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There is no publicly available global metric for comparing air traffic safety events in which two aircraft engage in a near-miss incident. Three separate and overlapping terms exist, and different air navigation service providers (ANSPs) may report and classify those events in separate ways. Based on the following review of current reporting metrics and severity classifications, werecommend that going forward, the UAS industry use the ICAO Airprox A+B metric as a starting point to develop a new, quantitative metric appropriate to high-density UAV operations.

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This is the first in a series of white papers documenting the process and findings behind Altiscope’s risk framework. Altiscope is collaboratively developing a quantitative risk model for present-day UAV mission profiles that will remain relevant as operations and technologies scale and evolve in the coming years. We explore where Altiscope’s risk model fits in the larger realm of UAV risk assessment, including how it relates to other industry and regulatory efforts and what the resulting model may look like. We’ve identified several high-level challenges, along with approaches we plan to take in tackling them. Finally, review existing research literature on UAV reliability and related issues.

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