Deep Ensemble for Rotorcraft Attitude Prediction
Hikmat Khan Nidhal Carla Bouaynaya Ghulam Rasool Tyler Travis Lacey Thompson Charles C. Johnson
Historically, the rotorcraft community has experienced a higher fatal
accident rate than other aviation segments, including commercial and general
aviation. Recent advancements in artificial intelligence (AI) and the
application of these technologies in different areas of our lives are both
intriguing and encouraging. When developed appropriately for the aviation
domain, AI techniques provide an opportunity to help design systems that can
address rotorcraft safety challenges. Our recent work demonstrated that AI
algorithms could use video data from onboard cameras and correctly identify
different flight parameters from cockpit gauges, e.g., indicated airspeed.
These AI-based techniques provide a potentially cost-effective solution,
especially for small helicopter operators, to record the flight state
information and perform post-flight analyses. We also showed that carefully
designed and trained AI systems could accurately predict rotorcraft attitude
(i.e., pitch and yaw) from outside scenes (images or video data). Ordinary
off-the-shelf video cameras were installed inside the rotorcraft cockpit to
record the outside scene, including the horizon. The AI algorithm could
correctly identify rotorcraft attitude at an accuracy in the range of 80\%. In
this work, we combined five different onboard camera viewpoints to improve
attitude prediction accuracy to 94\%. In this paper, five onboard camera views
included the pilot windshield, co-pilot windshield, pilot Electronic Flight
Instrument System (EFIS) display, co-pilot EFIS display, and the attitude
indicator gauge. Using video data from each camera view, we trained various
convolutional neural networks (CNNs), which achieved prediction accuracy in the
range of 79\% % to 90\% %. We subsequently ensembled the learned knowledge from
all CNNs and achieved an ensembled accuracy of 93.3\%.