Immersive Visualization in Pilot Training: from Cockpit Panels to Drone Navigation. Livatino, S., Morana, G., Iqbal, Y., Mohamed, M., Hwang, S., Gainley, P., Nguyen, T. H., Williams, K., & Zocco, A. In 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pages 454–458, Rome, Italy, October, 2022. IEEE. ![Immersive Visualization in Pilot Training: from Cockpit Panels to Drone Navigation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Training in aviation is a costly but needed activity. The sooner pilots become acquainted with flight instrumentation, procedures, and maneuvers, the earlier their performance becomes safe and effective and the more costsaving the training is. The advantage of using flight simulators is well understood and their use widespread. The use of virtual reality (VR) technologies promises to further speed up the training process, allowing for more realistic and varying scenarios and a stronger sense of being there. There is however some reluctance towards the use of immersive VR systems in aviation training and lack of knowledge on its effectiveness. The result is a slow take-up of VR solutions while the use of 2Dscreen monitors remains dominant. This paper aim is to assess the “VR advantage” for pilot training. Users’ performance on VR headset is compared to that on 2D monitors. The focus is on two types of operations: flight deck training, i.e. the different operations pilots need to learn at cockpit; and skillful teleoperation practice, i.e. conducting aerial vehicle driving. We experiment the first type of operation for two sets of tasks on a virtual 3D representation of a Cessna cockpit, and the second type of operation on a quadcopter drone driven in a narrow tunnel. The aim is to gather insight on user’s performance and sensations when using immersive VR technologies. User performance is objectively measured in terms of accuracy of operation and timing, whereas users’ sensations are subjectively rated in terms of sense of presence, visual comfort and ease of use.
@inproceedings{livatinoImmersiveVisualizationPilot2022,
address = {Rome, Italy},
title = {Immersive {Visualization} in {Pilot} {Training}: from {Cockpit} {Panels} to {Drone} {Navigation}},
isbn = {978-1-66548-574-6},
shorttitle = {Immersive {Visualization} in {Pilot} {Training}},
url = {https://ieeexplore.ieee.org/document/9967530/},
doi = {10.1109/MetroXRAINE54828.2022.9967530},
abstract = {Training in aviation is a costly but needed activity. The sooner pilots become acquainted with flight instrumentation, procedures, and maneuvers, the earlier their performance becomes safe and effective and the more costsaving the training is. The advantage of using flight simulators is well understood and their use widespread. The use of virtual reality (VR) technologies promises to further speed up the training process, allowing for more realistic and varying scenarios and a stronger sense of being there. There is however some reluctance towards the use of immersive VR systems in aviation training and lack of knowledge on its effectiveness. The result is a slow take-up of VR solutions while the use of 2Dscreen monitors remains dominant. This paper aim is to assess the “VR advantage” for pilot training. Users’ performance on VR headset is compared to that on 2D monitors. The focus is on two types of operations: flight deck training, i.e. the different operations pilots need to learn at cockpit; and skillful teleoperation practice, i.e. conducting aerial vehicle driving. We experiment the first type of operation for two sets of tasks on a virtual 3D representation of a Cessna cockpit, and the second type of operation on a quadcopter drone driven in a narrow tunnel. The aim is to gather insight on user’s performance and sensations when using immersive VR technologies. User performance is objectively measured in terms of accuracy of operation and timing, whereas users’ sensations are subjectively rated in terms of sense of presence, visual comfort and ease of use.},
language = {en},
urldate = {2023-04-11},
booktitle = {2022 {IEEE} {International} {Conference} on {Metrology} for {Extended} {Reality}, {Artificial} {Intelligence} and {Neural} {Engineering} ({MetroXRAINE})},
publisher = {IEEE},
author = {Livatino, Salvatore and Morana, Giuseppe and Iqbal, Yasir and Mohamed, Maya and Hwang, S. and Gainley, Phillip and Nguyen, T. H. and Williams, Kate and Zocco, Alessandro},
month = oct,
year = {2022},
pages = {454--458},
}
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