Enhancing situational awareness of helicopter pilots in unmanned aerial vehicle-congested environments using an airborne visual artificial intelligence approach

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dc.contributor.authorMugabe, John
dc.contributor.authorWisniewski, Mariusz
dc.contributor.authorPerrusquía, Adolfo
dc.contributor.authorGuo, Weisi
dc.date.accessioned2025-01-08T16:06:22Z
dc.date.available2025-01-08T16:06:22Z
dc.date.freetoread2025-01-08
dc.date.issued2024-12-04
dc.date.pubOnline2024-12-04
dc.description.abstractThe use of drones or Unmanned Aerial Vehicles (UAVs) and other flying vehicles has increased exponentially in the last decade. These devices pose a serious threat to helicopter pilots who constantly seek to maintain situational awareness while flying to avoid objects that might lead to a collision. In this paper, an Airborne Visual Artificial Intelligence System is proposed that seeks to improve helicopter pilots’ situational awareness (SA) under UAV-congested environments. Specifically, the system is capable of detecting UAVs, estimating their distance, predicting the probability of collision, and sending an alert to the pilot accordingly. To this end, we aim to combine the strengths of both spatial and temporal deep learning models and classic computer stereo vision to (1) estimate the depth of UAVs, (2) predict potential collisions with other UAVs in the sky, and (3) provide alerts for the pilot with regards to the drone that is likely to collide. The feasibility of integrating artificial intelligence into a comprehensive SA system is herein illustrated and can potentially contribute to the future of autonomous aircraft applications.
dc.description.journalNameSensors
dc.format.mediumElectronic
dc.identifier.citationMugabe J, Wisniewski M, Perrusquía A, Guo W. (2024) Enhancing situational awareness of helicopter pilots in unmanned aerial vehicle-congested environments using an airborne visual artificial intelligence approach. Sensors, Volume 24, Issue 23, December 2024, Article number 7762
dc.identifier.eissn1424-8220
dc.identifier.elementsID560122
dc.identifier.issn1424-8220
dc.identifier.issueNo23
dc.identifier.paperNo7762
dc.identifier.urihttps://doi.org/10.3390/s24237762
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23346
dc.identifier.volumeNo24
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/1424-8220/24/23/7762
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectsituational awareness
dc.subjectdeep learning
dc.subjectcomputer vision
dc.subjectstereo vision
dc.subjectStereoNet
dc.subjectLong Short-Term Memory (LSTM)
dc.subjectthreshold-based alert system
dc.subject40 Engineering
dc.subject4001 Aerospace Engineering
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject4605 Data Management and Data Science
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectLong Short-Term Memory (LSTM)
dc.subjectStereoNet
dc.subjectcomputer vision
dc.subjectdeep learning
dc.subjectsituational awareness
dc.subjectstereo vision
dc.subjectthreshold-based alert system
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleEnhancing situational awareness of helicopter pilots in unmanned aerial vehicle-congested environments using an airborne visual artificial intelligence approach
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-12-03

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