Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes

• URL: http://arxiv.org/abs/2103.04753v1
• Date: Thu, 4 Mar 2021 23:16:56 GMT
• Title: Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes
• Authors: Ngaire Underhill, Andrew Weinert
• Abstract summary: The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. New airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For many aviation safety studies, manned aircraft behavior is represented using dynamic Bayesian networks. The original statistical models were developed from 2008-2013 to support safety simulations for altitudes above 500 feet Above Ground Level (AGL). However, these models were not sufficient to assess the safety of smaller UAS operations below 500 feet AGL. In response, newer models with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different than models of fixed-wing aircraft to require type specific models. The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low altitude operations. We also address which models can be surrogates for noncooperative aircraft without transponders.

Related papers

• On the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models [2.7336487680215815]
  This paper investigates the generalization capabilities of deep learning models in predicting fuel consumption. We propose a novel methodology that integrates neural network architectures with domain generalization techniques. For previously unseen aircraft types, the introduction of noise into aircraft and engine parameters improved model generalization.
  arXiv  Detail & Related papers  (2024-10-10T08:34:19Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data [3.3748750222488657]
  We present a methodology for constructing data-driven maneuver generation models for agile aircraft. Our approach combines techniques from imitation learning, transfer learning, and reinforcement learning to achieve this objective.
  arXiv  Detail & Related papers  (2023-12-27T14:26:34Z)
• SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models [74.58014281829946]
  We analyze the effectiveness of several representative attacks/defenses, including model stealing attacks, membership inference attacks, and backdoor detection on public models. Our evaluation empirically shows the performance of these attacks/defenses can vary significantly on public models compared to self-trained models.
  arXiv  Detail & Related papers  (2023-10-19T11:49:22Z)
• Estimating See and Be Seen Performance with an Airborne Visual Acquisition Model [0.0]
  Separation provision and collision avoidance are fundamental components of layered conflict management system. Pilots have visual-based separation responsibilities to see and be seen to maintain separation between aircraft. Drone interactions with crewed aircraft should not be more hazardous than interactions between traditional aviation aircraft.
  arXiv  Detail & Related papers  (2023-06-29T11:39:10Z)
• Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures [52.25258289718559]
  We propose a probabilistic model that can learn the variability from procedural data and flight tracks collected from radar surveillance data. We show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft.
  arXiv  Detail & Related papers  (2023-03-17T13:58:06Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)
• A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data [0.0]
  Snow and ice on runway surfaces reduces tire-pavement friction needed for retardation and directional control. XGBoost is used to create a combined runway assessment system.
  arXiv  Detail & Related papers  (2021-07-01T11:01:13Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)
• Data-driven Method for Estimating Aircraft Mass from Quick Access Recorder using Aircraft Dynamics and Multilayer Perceptron Neural Network [4.828353666660018]
  Overloading an aircraft with passengers and baggage might result in a safety hazard. Airlines can use this tool to better utilize aircraft's payload.
  arXiv  Detail & Related papers  (2020-12-10T04:44:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.