With the continued development of unmanned aerial vehicle (UAV) technologies, the UAV on-board automation is increasingly more capable of performing tasks formerly done by human operators. Thereby, the role of UAVs is changing from being mere tools to become members of integrated manned-unmanned systems. However, the high automation necessary to achieve this cooperation, introduces a new set of negative effects for the human operator such as complacency or automation bias. Adaptive assistance is one approach to counteract these negative effects seen in human-automation-interaction. To enable adaptive assistance, we present a cognitive state estimation framework for a MUM-T aircraft application. The goal of this approach is to assess attention allocation and SA of a pilot in real-time and identify possible breakdowns in the situational picture that could cause performance decrements and errors. The design of a MUM-T cockpit simulator is presented to describe how this cognitive state estimation framework is integrated into a human-autonomy-teaming environment. The results of initial simulator experiments are presented and areas of further research are identified.

随着无人飞行器（UAV）技术的不断发展，无人飞行器的机载自动化越来越有能力执行以前由人类操作员完成的任务。因此，无人机的作用正从单纯的工具转变为无人驾驶综合系统的成员。但是，实现这种合作所需的高度自动化会给操作员带来一系列新的负面影响，例如自满或自动化偏差。自适应辅助是抵消人类自动化互动中出现的这些负面影响的一种方法。为了实现自适应协助，我们提出了针对MUM-T飞机应用的认知状态估计框架。这种方法的目标是实时评估飞行员的注意力分配和SA，并识别情况图中可能引起性能下降和错误的故障。提出了MUM-T座舱模拟器的设计，以描述该认知状态估计框架如何集成到人类自主团队环境中。介绍了初始模拟器实验的结果，并确定了进一步研究的领域。