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1. Situational Awareness-based Augmented Reality Instructional (ARI) Module for Building Evacuation

   https://doi.org/10.1109/VRW50115.2020.00020  

   Sharma, Sharad; Stigall, James; Bodempudi, Sri Teja (March 2020, 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW))

   Emergency response in indoor building evacuation is essential for effective rescue and safety management. First responders often lack the situational awareness capability to quickly assess the layout of a building upon initial entry. For occupants of the building, situational awareness becomes more important in cases of active shooter events or circumstances of fire and smoke. One of the challenges is to provide user-specific personalized evacuation routes in real-time. In multilevel building environments, the complexity of the architecture creates problems for both visual and mental representation of the 3D spaces. This paper presents three cutting edge Augmented Reality Instructional (ARI) modules that overcome the visual limitations associated with the traditional, static 2D methods of communicating evacuation plans for multilevel buildings. Using existing building features, the authors demonstrate how the three modules provide contextualized 3D visualizations that promote and support spatial knowledge acquisition and cognitive mapping thereby enhancing situational awareness. These ARI visualizations are developed for first responders and building occupants to help increase emergency preparedness and mitigate the evacuation related risks in multilevel building rescues and safety management. Specifically, the paper describes the design and implementation of the ARI modules and reports the results of the pilot studies conducted to evaluate their perceived usefulness, ease-of-use, and usability. The results suggest the desirability of further heuristic examination of three-dimensional situational awareness-based ARI application effectiveness in multilevel building evacuations. 

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2. Understanding Reliance Decisions in Automated Vehicles Using Random Forest Analysis

   https://doi.org/10.1177/10711813241262982  

   Ma, Xingjian; Xiao, Xizi; Mehta, Ranjana; McDonald, Anthony_D (August 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Driver reliance on automated vehicles (AV) is a critical component of safety particularly during high-risk traffic scenarios. Factors that influence reliance, including trust, situation awareness, fatigue, and demographics, have been independently explored; however, few analyses have investigated predicting AV reliance and compared factors comprehensively. The goals of this study were to develop a random forest (RF) model to predict reliance and to analyze the importance of factors for reliance decisions. We leveraged data from a driving simulation study where participants encountered four traffic events including responding to an illegal vehicle crossing, managing construction zones, stopping at a vandalized stop sign, and a pedestrian detection task. The dataset included reliance decisions and subjective assessments of dispositional trust, situational trust, fatigue, and workload. An RF model fit to the dataset using cross validation achieved an average AUC of 0.81 and accuracy of 0.77 and situational trust emerged as the most influential predictor. 

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3. Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

   https://doi.org/10.3390/electronics11193023  

   Perera, Sirani M.; Myers, Rodman J.; Sullivan, Killian; Byassee, Kyle; Song, Houbing; Madanayake, Arjuna (October 2022, Electronics)

   The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm. 

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4. Analytic Review of Using Augmented Reality for Situational Awareness

   https://doi.org/10.1109/TVCG.2022.3141585  

   Woodward, Julia; Ruiz, Jaime (January 2022, IEEE Transactions on Visualization and Computer Graphics)

   Situational awareness is the perception and understanding of the surrounding environment. Maintaining situational awareness is vital for performance and error prevention in safety critical domains. Prior work has examined applying augmented reality (AR) to the context of improving situational awareness, but has mainly focused on the applicability of using AR rather than on information design. Hence, there is a need to investigate how to design the presentation of information, especially in AR headsets, to increase users’ situational awareness. We conducted a Systematic Literature Review to research how information is currently presented in AR, especially in systems that are being utilized for situational awareness. Comparing current presentations of information to existing design recommendations aided in identifying future areas of design. In addition, this survey further discusses opportunities and challenges in applying AR to increasing users’ situational awareness. 

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5. Mobile AR Application for Navigation and Emergency Response

   Mannuru, Nishith Reddy; Kanumuru, Mounica; Sharma, Sharad (January 2022, 2022 International Conference on Computational Science and Computational Intelligence (CSCI))

   Emergency response, navigation, and evacuation are key essentials for effective rescue and safety management. Situational awareness is a key ingredient when fire responders or emergency response personnel responds to an emergency. They have to quickly assess the layout of a building or a campus upon entry. Moreover, the occupants of a building or campus also need situational awareness for navigation and emergency response. We have developed an integrated situational awareness mobile augmented reality (AR) application for smart campus planning, management, and emergency response. Through the visualization of integrated geographic information systems and real-time data analysis, our mobile application provides insights into operational implications and offers information to support effective decision-making. Using existing building features, the authors demonstrate how the mobile AR application provides contextualized 3D visualizations that promote and support spatial knowledge acquisition and cognitive mapping thereby enhancing situational awareness. A limited user study was conducted to test the effectiveness of the proposed mobile AR application using the mobile phone usability questionnaire (MPUQ) framework. The results show that the mobile AR application was relatively easy to use and that it can be considered a useful application for navigation and evacuation. 

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