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Combating spatial disorientation in a dynamic self-stabilization task using AI assistants

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dc.contributor.authorMannan, Sheikh Abdul, author
dc.contributor.authorHansen, Paige, author
dc.contributor.authorVimal, Vivekanand Pandey, author
dc.contributor.authorDavies, Hannah N., auhtor
dc.contributor.authorDiZio, Paul, author
dc.contributor.authorKrishnaswamy, Nikhil, author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-12-17T19:12:10Z
dc.date.available2024-12-17T19:12:10Z
dc.date.issued2024-11-24
dc.description.abstractSpatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationSheikh Abdul Mannan, Paige Hansen, Vivekanand Pandey Vimal, Hannah N. Davies, Paul DiZio, and Nikhil Krishnaswamy. 2024. Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants. In International Conference on Human-Agent Interaction (HAI '24), November 24–27, 2024, Swansea, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3687272.3688329
dc.identifier.doihttps://doi.org/10.1145/3687272.3688329
dc.identifier.urihttps://hdl.handle.net/10217/239728
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights©Sheikh Abdul Mannan, et al. ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in HAI '24, https://dx.doi.org/10.1145/3687272.3688329 .
dc.subjectspatial disorientation
dc.subjectbalancing
dc.subjectAI assistance
dc.subjectreal-time human-agent interaction
dc.titleCombating spatial disorientation in a dynamic self-stabilization task using AI assistants
dc.typeText

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