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For human autonomy teaming, information for promoting transparency could lead to information overload, negatively impacting performance and workload. This paper presents an empirical study investigating how different level of details (LODs) about the autonomy represented on the user interface would influence speed, accuracy, and workload. Specifically, we compared visualizations of a lost person model at four different LODs to aid in directing human and unmanned aerial vehicles searchers in search and rescue missions. The lowest LOD was found to support higher accuracy but at the expense of speed. The highest LOD induced the highest workload, while the other three LODs induced lower and similar levels of workload. The results indicate that the LOD in transparent displays could induce a speed and accuracy tradeoff. 

This alternative format session provides a forum for human factors scholars and practitioners to showcase how state-of-the-art extended reality (XR) applications are being used in academia, defense, and industry to address human factors research. The session will begin with short introductions from each presenter to describe their XR application. Afterward, session attendees will engage with the presenters and their demonstrations, which will be set up around the demonstration floor room. This year’s showcase features XR applications in STEM education, medical and aviation training, agricultural data visualization, homeland security, training design, and visitor engagement in informal learning settings. Our goal is for attendees to experience how human factors professionals use XR to support human factors-oriented research and to learn about the exciting work being conducted with these emerging technologies. 

Full-body, powered wearable exoskeletons combine the capabilities of machines and humans to maximize productivity. Powered exoskeletons can ease industrial workers in manipulating heavy loads in a manner that is difficult to automate. However, introduction of exoskeletons may result in unexpected work hazards, due to the mismatch between user-intended and executed actions thereby creating difficulties in sensing the physical operational envelope, need for increased clearance, and maneuverability limitations. To control such hazards, this paper presents a rearview human localization augmented reality (AR) platform to enhance spatial awareness of people behind the exoskeleton users. This platform leverages a computer vision algorithm called Monocular 3D Pedestrian Localization and Uncertainty Estimation (MonoLoco) for identifying humans and estimating their distance from a video camera feed and off-the-shelf AR goggles for visualizing the surrounding. Augmenting rear view awareness of humans can help exoskeleton users to avoid accidental collisions that can lead to severe injuries.