在低度和高度自动化中预测回归手工性能
Natalie Griffiths1, Vanessa K Bowden1, Serena Wee1
1The University of Western Australia, Australia.
Human factors
|February 27, 2025
概括
操作员的工作量,疲劳和对自动化的信任在自动化故障后返回手动控制时显著预测性能. 适应性系统可以使用这些操作状态来最大限度地减少性能下降.
科学领域:
- 人与计算机的交互
- 认知工程 认知工程
- 自动化和控制系统自动化和控制系统
背景情况:
- 操作员状态如工作负载,疲劳和对自动化的信任对于安全的系统运行至关重要.
- 有限的研究存在于这些状态中的人内变化如何在自动化失败后预测返回手动 (RTM) 性能.
- 了解这些关系对于设计适应性工作系统至关重要,这些系统可以考虑性能下降和操作员疲劳.
研究的目的:
- 调查操作员状态变量 (工作量,疲劳,对自动化的信任,任务参与) 作为RTM性能预测因素.
- 确定自动化程度 (DOA) 是否会缓和操作员状态和RTM性能之间的关系.
主要方法:
- 参与者执行了一个模拟的空中交通控制任务,使用更高或更低程度的自动化 (DOA) 进行冲突检测.
- 当自动化无法解决冲突,需要手动干预时,评估了RTM的性能.
- 经营者状态 (工作量,疲劳,信任,参与) 通过自我报告定期测量.
主要成果:
- 与较高的DOA相比,较低的DOA导致更快的RTM性能;DOA没有缓解操作员状态效应.
- 增加的工作量和疲劳与较差的RTM准确性有关 (解决的冲突较少).
- 对自动化的更高信任与改善的RTM准确性相关.
结论:
- 操作员状态,特别是工作量,疲劳和信任,是RTM性能的重要预测因素.
- 虽然运营商状态预测性能,但由于研究中的不一致性,需要进一步研究.
- 适应性工作系统可以利用这些发现来缓解自动化故障期间的性能下降.
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