标题： 人类机器人团队中动态情境意识的框架：访谈研究 显示英文标题

标题： A Framework for Dynamic Situational Awareness in Human Robot Teams: An Interview Study

摘要： 在人机团队中，人类的情境意识是操作员对团队状态、行动、计划及其环境的有意识知识。适当的人类情境意识对于成功的人机协作至关重要。在人机团队合作中，通常假设最佳且所需的情境意识水平是随时了解一切。这种观点存在问题，因为为了实现最佳团队表现，人类需要知道的内容会根据动态环境条件、任务背景以及团队成员的角色和能力而变化。我们通过采访16名在多样化人机团队应用中具有主动和重复经验的参与者来探讨这一主题。基于这些访谈的分析，我们得出一个框架，解释了人机团队中所需情境意识的动态性质。此外，我们确定了一系列影响所需和实际情境意识水平（即动态情境意识）动态性质的因素，由实际和所需情境意识之间的差距导致的情境意识低效类型，以及它们的主要后果。我们还揭示了由人类和机器人发起的各种策略，这些策略有助于保持所需的情境意识。我们的研究结果为准确估计动态情境意识的实现和设计用户自适应的人机界面提供了依据。因此，这项工作有助于未来更协作和高效的人机团队的设计。 显示英文摘要

摘要： In human-robot teams, human situational awareness is the operator's conscious knowledge of the team's states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human-robot collaboration. In human-robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human-robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human-robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human-robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human-robot teams.