Abstract
Spatial 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.