Drone trajectory segmentation for real-time and adaptive time-of-flight prediction
This paper presents a method developed to predict the flight-time employed by a drone to
complete a planned path adopting a machine-learning-based approach. A generic path is …
complete a planned path adopting a machine-learning-based approach. A generic path is …
An explainable artificial intelligence (xAI) framework for improving trust in automated ATM tools
CS Hernandez, S Ayo… - 2021 IEEE/AIAA 40th …, 2021 - ieeexplore.ieee.org
With the increased use of intelligent Decision Support Tools in Air Traffic Management
(ATM) and inclusion of non-traditional entities, regulators and end users need assurance …
(ATM) and inclusion of non-traditional entities, regulators and end users need assurance …
Improving Algorithm Conflict Resolution Manoeuvres with Reinforcement Learning
M Ribeiro, J Ellerbroek, J Hoekstra - Aerospace, 2022 - mdpi.com
Future high traffic densities with drone operations are expected to exceed the number of
aircraft that current air traffic control procedures can control simultaneously. Despite …
aircraft that current air traffic control procedures can control simultaneously. Despite …
Neural network-based aircraft conflict prediction in final approach maneuvers
R Casado, A Bermúdez - Electronics, 2020 - mdpi.com
Conflict detection and resolution is one of the main topics in air traffic management.
Traditional approaches to this problem use all the available information to predict future …
Traditional approaches to this problem use all the available information to predict future …
[PDF][PDF] Personalized and transparent ai support for atc conflict detection and resolution: an empirical study
Artificial Intelligence provides both opportunities and considerable challenges to the
continued growth of Air Traffic Control (ATC) services. This paper presents a study where a …
continued growth of Air Traffic Control (ATC) services. This paper presents a study where a …
Machine Learning Attempt to Conflict Detection for UAV with System Failure in U-Space: Recurrent Neural Network, RNNn
R Komatsu, AAA Bechina, S Güldal… - … on Unmanned Aircraft …, 2022 - ieeexplore.ieee.org
U-Space services will be used by millions of Unmanned Aerial Vehicles (UAVs) also called
drones in the near future. To respond to growing demand, urban airspace needs more …
drones in the near future. To respond to growing demand, urban airspace needs more …
Preliminary design of an ATC support tool for the implementation of the Ad Hoc Separation Minima concept in an en-route sector.
L Serrano-Mira, LP Sanz… - Journal of Physics …, 2024 - iopscience.iop.org
The current aim in response to the anticipated growth in air traffic demand is to expand
airspace capacity. However, this poses a challenge, as many airspace volumes are nearing …
airspace capacity. However, this poses a challenge, as many airspace volumes are nearing …
Lateral and vertical air traffic control under uncertainty using reinforcement learning
Air traffic demand has increased at an unprecedented rate in the last decade (albeit
interrupted by the COVID pandemic), but capacity has not increased at the same rate …
interrupted by the COVID pandemic), but capacity has not increased at the same rate …
Machine Learning classification techniques applied to static air traffic conflict detection
JA Pérez-Castán, L Pérez-Sanz… - IOP Conference …, 2022 - iopscience.iop.org
Abstract This article evaluates Machine Learning (ML) classification techniques applied to
air-traffic conflict detection. The methodology develops a static approach in which the conflict …
air-traffic conflict detection. The methodology develops a static approach in which the conflict …
Machine Learning experiments for design purposes in air-traffic conflict-detection tool
JA Pérez-Castán, CF San Martín-Gueimunde… - Transportation research …, 2023 - Elsevier
Abstract Machine learning (ML) is the ability of computers to learn mathematical relations to
make predictions based on historical data. In recent years, ML has evolved rapidly and has …
make predictions based on historical data. In recent years, ML has evolved rapidly and has …