Enhancing situational awareness of helicopter pilots in unmanned aerial vehicle-congested environments using an airborne visual artificial intelligence approach - Mugabe, John; Wisniewski, Mariusz; Perrusquía, Adolfo; Guo, Weisi (MDPI, 2024)
The 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.
Keywords: situational awareness; deep learning; computer vision; stereo vision; StereoNet; Long Short-Term Memory (LSTM); threshold-based alert system; 40 Engineering; 4001 Aerospace Engineering; 46 Information and Computing Sciences; 4602 Artificial Intelligence; 4605 Data Management and Data Science; Machine Learning and Artificial Intelligence; Analytical Chemistry; 3103 Ecology; 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4104 Environmental management; 4606 Distributed computing and systems software