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AI Inspired ATC, Based on ANN and Using NLP
Technical Paper
2023-01-0985
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Event:
2023 AeroTech
Language:
English
Abstract
An Air Traffic Controller(ATC) is a person responsible for the proper Take-Off
and Landing of an Aircraft from the runway, and for relaying continuous vital
information back and forth from Pilots. The proposed ATC will automate this
entire process to reduce human-generated errors and save costs. The entire
system will be made using Artificial Intelligence and will use Natural Language
Processing and Artificial Neural Networks to create a human-like, but a
better-prepared system. The model needed to create the ATC, can be trained on
already available crucial flight data. The data must include flight take-off and
landing time, along with altered time based on weather, climate and other
physical factors. The back-end system of the ATC, can be then made to work on
this trained model, and produce correct and calculated flight path and timings
for the take-off and Landing. The system will do an automatic Pre-flight
checkup, based on weather and other clear-sky conditions, such as birds and
overhead flights. If there are no problematic conditions, a flight can be
allowed to take-off. Similarly, a flight can be allowed to land, based on a
clear runaway and good weather conditions. Also, the system will use Artificial
Neural Networks, to pan out an optimized flight path for the aircraft to follow,
so as to reach a particular destination by avoiding extra air traffic, and
saving fuel.The AI-inspired ATC will also be responsible, to tackle problems
faced by pilots based on their requests, voice & mood conditions, which will
be processed using a customized NLP Component. Implementing the proposed
AI-inspired Air Traffic Controller can significantly reduce errors, save costs,
and reduce the overhead of extra time in panic situations.
Authors
Topic
Citation
Aman, E., Jana, S., Athikary, K., and Suryanarayana, R., "AI Inspired ATC, Based on ANN and Using NLP," SAE Technical Paper 2023-01-0985, 2023, https://doi.org/10.4271/2023-01-0985.Also In
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