AI助手可提升飞行员抗空间定向障碍能力，强化学习模型效果最佳但信任度低。
Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants
- 用数字孪生模拟不同水平飞行员表现，训练AI提供纠正提示。
- 强化学习助手降低事故率37%，平均偏离距离减少42%。
- 虽性能更优，但人类更信赖规则型助手，存在信任鸿沟。
空间定向障碍是导致飞机致命事故的主要原因。本文探索了人工智能代理在动态自稳定任务中辅助飞行员维持平衡、预防失控的能力，通过提供提示和纠正措施缓解空间定向障碍。研究使用多轴旋转系统（MARS）采集人类在类太空飞行条件下的自平衡数据，基于此训练出代表不同熟练程度的“数字孪生”模型。随后，采用强化学习与深度学习模型训练出能在预测失控时提供纠正提示的助手。数字孪生与助手模型在物理参数相同的虚拟倒立摆（VIP）任务中协同运行。根据任务指标如坠毁频率和平均偏离平衡方向距离，筛选出5个表现最优的助手。这些助手被用于与20名新受试者进行共演实验，任务中空间信息被刻意削弱。结果表明，部分AI助手显著提升了人类表现；其中基于强化学习的助手在客观指标上更优，但人类主观评价其可信度与偏好度更低。
原文摘要 · Abstract (English)
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.
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