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First Responders' Perceptions of Semantic Information for Situational Awareness in Robot-Assisted Emergency Response

Tianshu Ruan, Zoe Betta, Georgios Tzoumas, Rustam Stolkin, Manolis Chiou

TL;DR

The study surveyed 22 first responders across eight countries to assess attitudes toward semantic information for situational awareness in robot-assisted emergency response. It found generally positive views toward robots and meaningful perceived value of semantic information for SA and forecasting, with trust and usefulness tied to modest accuracy thresholds. Results highlight a field-lab gap and identify preferred semantic types and multimodal delivery methods, underscoring the need for closer FR-researcher collaboration to develop practical, end-user-aligned semantic SA systems. The work provides cross-national, end-user perspectives to guide the design and deployment of semantically aware robotic systems in extreme environments.

Abstract

This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enhanced SA. Results show that most FRs expressed positive attitudes toward robots, and rated the usefulness of semantic information for building SA at an average of 3.6 out of 5. Semantic information was also valued for its role in predicting unforeseen emergencies (mean 3.9). Participants reported requiring an average of 74.6\% accuracy to trust semantic outputs and 67.8\% for them to be considered useful, revealing a willingness to use imperfect but informative AI support tools. To the best of our knowledge, this study offers novel insights by being one of the first to directly survey FRs on semantic-based SA in a cross-national context. It reveals the types of semantic information most valued in the field, such as object identity, spatial relationships, and risk context-and connects these preferences to the respondents' roles, experience, and education levels. The findings also expose a critical gap between lab-based robotics capabilities and the realities of field deployment, highlighting the need for more meaningful collaboration between FRs and robotics researchers. These insights contribute to the development of more user-aligned and situationally aware robotic systems for emergency response.

First Responders' Perceptions of Semantic Information for Situational Awareness in Robot-Assisted Emergency Response

TL;DR

The study surveyed 22 first responders across eight countries to assess attitudes toward semantic information for situational awareness in robot-assisted emergency response. It found generally positive views toward robots and meaningful perceived value of semantic information for SA and forecasting, with trust and usefulness tied to modest accuracy thresholds. Results highlight a field-lab gap and identify preferred semantic types and multimodal delivery methods, underscoring the need for closer FR-researcher collaboration to develop practical, end-user-aligned semantic SA systems. The work provides cross-national, end-user perspectives to guide the design and deployment of semantically aware robotic systems in extreme environments.

Abstract

This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enhanced SA. Results show that most FRs expressed positive attitudes toward robots, and rated the usefulness of semantic information for building SA at an average of 3.6 out of 5. Semantic information was also valued for its role in predicting unforeseen emergencies (mean 3.9). Participants reported requiring an average of 74.6\% accuracy to trust semantic outputs and 67.8\% for them to be considered useful, revealing a willingness to use imperfect but informative AI support tools. To the best of our knowledge, this study offers novel insights by being one of the first to directly survey FRs on semantic-based SA in a cross-national context. It reveals the types of semantic information most valued in the field, such as object identity, spatial relationships, and risk context-and connects these preferences to the respondents' roles, experience, and education levels. The findings also expose a critical gap between lab-based robotics capabilities and the realities of field deployment, highlighting the need for more meaningful collaboration between FRs and robotics researchers. These insights contribute to the development of more user-aligned and situationally aware robotic systems for emergency response.
Paper Structure (10 sections, 5 figures)

This paper contains 10 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. Methodology
  4. Results
  5. FR background and demographics
  6. General attitudes towards robots
  7. Views about SA and use of semantic information
  8. Analysis and discussion
  9. Conclusion
  10. Acknowledgment

Figures (5)

  • Figure 1: Distributions of role, field experience, education level, and robot deployment level by age group. Robot deployment level refers to the frequency of using robots in the mission. Higher level means higher frequency.
  • Figure 2: Distribution of general attitudes towards robots on personal and societal levels. Most points are under the diagonal line (in the green zone). This indicates that most FRs have positive attitudes towards robots in both personal and societal contexts.
  • Figure 3: Score distributions of numerical questions, including: SA level before mission (Q43); Over workload experience (Q47); Semantics help SA? (Q49); Semantics used level currently (Q50); Semantics help prediction? (Q53); If AI understands the situation, helpful? (Q54)
  • Figure 5: Score distribution by role: crosses refer to the median in the group. In each question, the roles displayed from left to right are operator, supervisor, commander, and domain specialist.
  • Figure 8: Important semantics for FRs in the mission according to the questionnaire.