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

2025-01-15

AI Summary

Human–machine collaboration suffers from dynamic mismatches between situational awareness (SA) supply and demand, as conventional SA models assume static, omniscient awareness—failing to account for real-time fluctuations in environment, task, and role. Method: Drawing on thematic analysis of in-depth interviews with 16 domain experts, this study proposes the concept of *dynamic situational awareness* and establishes the first theoretical framework explicating how SA requirements evolve contextually and temporally. Contribution/Results: The framework identifies critical factors governing SA supply–demand alignment, categorizes SA failure modes and their operational consequences, and prescribes collaborative maintenance strategies. It yields (1) an operationalizable dynamic SA assessment framework and (2) evidence-based design guidelines for adaptive human–machine interfaces. Validated in collaborative systems, the framework enables user-adaptive interface development, significantly enhancing both effectiveness and safety of human–machine teaming.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• situational awareness
• human-robot collaboration
• dynamic adjustment

Innovation

• Dynamic Situational Awareness Framework
• Human-Robot Team Performance
• Effective Strategies for Situational Awareness Enhancement