The Black Swan: The Impact of the Highly Improbable

Airspace encounter models, providing a statistical representation of geometries and aircraft behavior during a close encounter, are required to estimate the safety and robustness of collision avoidance systems. Prior encounter models, developed to certify the Traffic Alert and Collision Avoidance System, have been limited in their ability to capture important characteristics of encounters as revealed by recorded surveillance data, do not capture the current mix of aircraft types or noncooperative aircraft, and do not represent more recent airspace procedures. This paper describes a methodology for encounter model construction based on a Bayesian statistical framework connected to an extensive set of national radar data. In addition, this paper provides examples of using several such high-fidelity models to evaluate the safety of collision avoidance systems for manned and unmanned aircraft.

Airspace Encounter Models for Estimating Collision Risk

A key challenge associated with integration of Unmanned Aircraft Systems (UAS) is developing a means to sense and avoid (SAA) other aircraft. One of the main functions of SAA is to remain “well clear” of other aircraft. While human pilots determine “well clear” subjectively, SAA systems need a clear quantitative definition of UAS Well Clear. The Sense and Avoid Science and Research Panel (SARP) brought together key experts, determined guiding principles for UAS Well Clear, and aligned research efforts from NASA, Massachusetts Institute of Technology (MIT) Lincoln Laboratory, and the U.S. Air Force Research Laboratory to evaluate three UAS Well Clear candidates in four modelling and simulation environments. The three Well Clear candidates were evaluated against eight agreed evaluation metrics. The result of the SARP evaluation process led to a recommended quantitative definition for UAS Well Clear, promising to close one of the most urgent research gaps for UAS SAA.

Defining Well Clear for Unmanned Aircraft Systems

Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. Dur...

https://arc.aiaa.org/doi/pdf/10.2514/1.D0091

Well-Clear Recommendation for Small Unmanned Aircraft Systems Based on Unmitigated Collision Risk

Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct off-line performance and safety analyses. In this paper, we develop a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport. The method fits the model based on a historical dataset of radar-based position measurements of aircraft landings and takeoffs at that airport. We find that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of the aircraft trajectories. Furthermore, the model trains quickly, is compact, and allows for efficient real-time inference.

pdf/learning-probabilistic-trajectory-models-of-aircraft-in-1waz4etfyi.pdf

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data

: Airspace encounter models, which provide realistic close encounter situations representative of the airspace, are a critical component in the safety assessment of sense and avoid systems. Of particular relevance to Unmanned Aircraft Systems is the potential for encountering general aviation aircraft that are flying under Visual Flight Rules and which may not be in contact with air traffic control. In response to the need to develop a model of these types of encounters, Lincoln Laboratory undertook an extensive radar data collection and modeling effort involving over 200 radars across the United States. This report describes the structure, content, and usage of that encounter model. The model is based on the use of Bayesian networks to represent relationships between dynamic variables and to construct random aircraft trajectories that are statistically similar to those observed in the radar data. The result is a framework from which representative intruder trajectories can be generated and used in fast-time Monte Carlo simulations to provide accurate estimates of collision risk. This report includes updates to the scope and data processing methods used for the initial version of the uncorrelated encounter model. The updates are motivated by increasing interest in sense and avoid applications related to self-separation in addition to collision avoidance. Self-separation encounter models are likely to have stricter requirements, including the need to simulate longer encounters and greater look-ahead time. Another enhancement to the model is an additional discrete variable specifying the geographic location, which allows a single model to provide specialized coverage of different geographic regions. These regions include the contiguous United States, Alaska, islands, and offshore areas.

https://apps.dtic.mil/sti/pdfs/ADA589697.pdf

Uncorrelated Encounter Model of the National Airspace System, Version 2.0

/pdf/correlated-encounter-model-for-cooperative-aircraft-in-the-1n9jygblxc.pdf

Correlated Encounter Model for Cooperative Aircraft in the National Airspace System; Version 2.0

This report provides a brief review of major recent risk-based modeling (RBM) approaches, with particular emphasis on how these tools can be applied during initial Next Generation Air Transportation System (NextGen) concept development and how their use can be validated. Effective safety analysis should begin as early as possible during a systems life cycle in order to have maximum impact. Ideally, safety considerations should play a role even during a new systems concept definition and development. Elements of NextGen are currently progressing through these early phases. NextGen will increasingly rely on integrating multiple systems and information sources together to enable improved efficiency, safety, and reduced environmental impact. Ensuring that such complex interconnected systems are developed to meet safety goals requires corresponding advances in RBM and safety assessment approaches. This report does not cover the more detailed safety analyses that must be applied to mature system concepts. Rather, the focus is on approaches for hazard identification, scoping, and coarse risk estimation for systems in the early conceptual development stage, when details on the design and operation of the system have yet to be resolved. Risk models applied in this constrained context cannot be expected to provide the same complete, quantitative results as they do for mature systems. Following a review of prior models, this report continues with recommendations for RBM development, application, validation, and coordination between NextGen efforts. Also, a discussion on safety and concept development is provided.

Risk-Based Modeling to Support NextGen Concept Assessment and Validation

Encounter models represent the distribution of aircraft encounter situations where an unmanned aircraft sense and avoid system would act. Therefore, encounter models are central to the Monte Carlo simulation safety evaluation of sense and avoid self separation and collision avoidance. Previous encounter models were intended to evaluate collision avoidance and were never demonstrated to be sufficient for evaluating self separation. This paper describes how the original models were extended and validated to enable self separation evaluation. The paper also compares the model against observed encounters where the model is shown to accurately reflect observed encounters.

Extended Airspace Encounter Models for Unmanned Aircraft Sense and Avoid Safety Evaluation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

E Harkleroad

Author Tools

Chat about Author

Papers

Uncorrelated Encounter Model of the National Airspace System, Version 2.0

Correlated Encounter Model for Cooperative Aircraft in the National Airspace System; Version 2.0

Risk-Based Modeling to Support NextGen Concept Assessment and Validation

Extended Airspace Encounter Models for Unmanned Aircraft Sense and Avoid Safety Evaluation