Machine Learning for Drone Conflict Prediction: Simulation Results

Brian Hilburn

2022

Abstract

Introducing drones into urban airspace poses several air traffic management (ATM) challenges. Among these is how to monitor and de-conflict (potentially high-density / low predictability) drone traffic. This task might exceed the capabilities of the current (human-based) air traffic control system. One potential solution lies in the use of Machine Learning (ML) to predict drone conflicts. This study explored via low-fidelity offline simulations the potential benefits of ML for drone conflict prediction, specifically: how well can a simple ML model predict on the basis of instantaneous traffic pattern snapshot, whether that pattern will result in an eventual airspace conflict? Secondly, how is model performance impacted by such parameters as traffic level, traffic predictability, and ‘look-ahead’ time of the model? Using a deep learning neural network approach, this study experimentally manipulated traffic load, traffic predictability, and look-ahead time. Using limited trajectory data (aircraft state) and a limited neural network architecture, results demonstrated (especially with structured traffic) large potential ML benefits on airspace conflict prediction. Binary classification accuracy generally exceeded 90%, and error under the most demanding scenarios tended toward false positive (i.e. incorrectly predicting a conflict). The current work is abstracted from Hilburn (2020), which provides further detail.

Paper Citation

in Harvard Style

Hilburn B. (2022). Machine Learning for Drone Conflict Prediction: Simulation Results. In Proceedings of the 1st International Conference on Cognitive Aircraft Systems - Volume 1: ICCAS; ISBN 978-989-758-657-6, SciTePress, pages 77-82. DOI: 10.5220/0011963700003622

in Bibtex Style

@conference{iccas22,
author={Brian Hilburn},
title={Machine Learning for Drone Conflict Prediction: Simulation Results},
booktitle={Proceedings of the 1st International Conference on Cognitive Aircraft Systems - Volume 1: ICCAS},
year={2022},
pages={77-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011963700003622},
isbn={978-989-758-657-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Cognitive Aircraft Systems - Volume 1: ICCAS
TI - Machine Learning for Drone Conflict Prediction: Simulation Results
SN - 978-989-758-657-6
AU - Hilburn B.
PY - 2022
SP - 77
EP - 82
DO - 10.5220/0011963700003622
PB - SciTePress