Mugabe, JohnWisniewski, MariuszPerrusquía, AdolfoGuo, Weisi2025-01-082025-01-082024-12-04Mugabe 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 77621424-8220https://doi.org/10.3390/s24237762https://dspace.lib.cranfield.ac.uk/handle/1826/23346The 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.ElectronicenAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/situational awarenessdeep learningcomputer visionstereo visionStereoNetLong Short-Term Memory (LSTM)threshold-based alert system40 Engineering4001 Aerospace Engineering46 Information and Computing Sciences4602 Artificial Intelligence4605 Data Management and Data ScienceMachine Learning and Artificial IntelligenceAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareArticle1424-822056012277622423