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1. An Empirical Evaluation of the Calibration of Auditory Distance Perception under Different Levels of Virtual Environment Visibilities

   https://doi.org/10.1109/VR58804.2024.00089  

   Lin, Wan-Yi; Venkatakrishnan, Rohith; Venkatakrishnan, Roshan; Babu, Sabarish V; Pagano, Christopher; Lin, Wen-Chieh (March 2024, IEEE)

   The perception of distance is a complex process that often involves sensory information beyond that of just vision. In this work, we investigated if depth perception based on auditory information can be calibrated, a process by which perceptual accuracy of depth judgments can be improved by providing feedback and then performing corrective actions. We further investigated if perceptual learning through carryover effects of calibration occurs in different levels of a virtual environment’s visibility based on different levels of virtual lighting. Users performed an auditory depth judgment task over several trials in which they walked where they perceived an aural sound to be, yielding absolute estimates of perceived distance. This task was performed in three sequential phases: pretest, calibration, posttest. Feedback on the perceptual accuracy of distance estimates was only provided in the calibration phase, allowing to study the calibration of auditory depth perception. We employed a 2 (Visibility of virtual environment) ×3 (Phase) ×5 (Target Distance) multi-factorial design, manipulating the phase and target distance as within-subjects factors, and the visibility of the virtual environment as a between-subjects factor. Our results revealed that users generally tend to underestimate aurally perceived distances in VR similar to the distance compression effects that commonly occur in visual distance perception in VR. We found that auditory depth estimates, obtained using an absolute measure, can be calibrated to become more accurate through feedback and corrective action. In terms of environment visibility, we find that environments visible enough to reveal their extent may contain visual information that users attune to in scaling aurally perceived depth. 

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2. WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

   Yao, Shunyu; Chen, Howard; Yang, John; Narasimhan, Karthik (January 2022, Advances in neural information processing systems)

   Most existing benchmarks for grounding language in interactive environments either lack realistic linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. We develop WebShop – a simulated e-commerce website environment with 1.18 million real-world products and 12,087 crowd-sourced text instructions. In this environment, an agent needs to navigate multiple types of webpages and issue diverse actions to find, customize, and purchase a product given an instruction. WebShop provides several challenges including understanding compositional instructions, query (re-)formulation, dealing with noisy text in webpages, and performing strategic exploration. We collect over 1,600 human trajectories to first validate the benchmark, then train and evaluate a diverse range of agents using reinforcement learning, imitation learning, and pre-trained image and language models. Our best model achieves a task success rate of 29%, which significantly outperforms rule heuristics but is far lower than expert human performance (59%). We also analyze agent and human trajectories and ablate various model components to provide insights for developing future agents with stronger language understanding and decision making abilities. Finally, we show our agent trained on WebShop exhibits non-trivial sim-to-real transfer when evaluated on amazon.com and ebay.com, indicating the potential value of our benchmark for developing practical web agents that can operate in the wild. 

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3. The interaction between map complexity and crowd movement on navigation decisions in virtual reality

   https://doi.org/10.1098/rsos.191523  

   Zhao, Hantao; Thrash, Tyler; Grossrieder, Armin; Kapadia, Mubbasir; Moussaïd, Mehdi; Hölscher, Christoph; Schinazi, Victor R. (March 2020, Royal Society Open Science)

   A carefully designed map can reduce pedestrians’ cognitive load during wayfinding and may be an especially useful navigation aid in crowded public environments. In the present paper, we report three studies that investigated the effects of map complexity and crowd movement on wayfinding time, accuracy and hesitation using both online and laboratory-based networked virtual reality (VR) platforms. In the online study, we found that simple map designs led to shorter decision times and higher accuracy compared to complex map designs. In the networked VR set-up, we found that co-present participants made very few errors. In the final VR study, we replayed the traces of participants’ avatars from the second study so that they indicated a different direction than the maps. In this scenario, we found an interaction between map design and crowd movement in terms of decision time and the distributions of locations at which participants hesitated. Together, these findings can help the designers of maps for public spaces account for the movements of real crowds. 

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4. Multi-agent Crowd Simulation in an Active Shooter Environment

   https://doi.org/10.1007/978-3-031-06015-1_8  

   Sharma, S. (May 2022, Lecture notes in computer science)

   Chen, J.Y.C. (Ed.)

   In recent years there has been a sharp increase in active shooter events, but there has been no introduction of new technology or tactics capable of increasing preparedness and training for active shooter events. This has raised a major concern about the lack of tools that would allow robust predictions of realistic human movements and the lack of understanding about the interaction in designated simulation environments. It is impractical to carry out live experiments where thousands of people are evacuated from buildings designed for every possible emergency condition. There has been progress in understanding human movement, human motion synthesis, crowd dynamics, indoor environments, and their relationships with active shooter events, but challenges remain. This paper presents a virtual reality (VR) experimental setup for conducting virtual evacuation drills in response to extreme events and demonstrates the behavior of agents during an active shooter environment. The behavior of agents is implemented using behavior trees in the Unity gaming engine. The VR experimental setup can simulate human behavior during an active shooter event in a campus setting. A presence questionnaire (PQ) was used in the user study to evaluate the effectiveness and engagement of our active shooter environment. The results show that majority of users agreed that the sense of presence was increased when using the emergency response training environment for a building evacuation environment. 

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5. Modeling competing guidance on evacuation choices under time pressure using virtual reality and machine learning

   https://doi.org/10.1016/j.eswa.2024.125582  

   Zhang, Yuxin; Kinateder, Max; Huang, Xinyan; Warren, William H (March 2025, Expert Systems with Applications)

   Efficient emergency guidance in buildings is essential for the safe evacuation of occupants. However, occupants may be exposed to contradictory information from signage and other sources of information. This study presents a set of forced-choice VR experiments and a machine learning approach to investigate the effect of competing or conflicting guidance on exit choice in simulated scenarios. In the VR study, participants chose between two potential exits under time pressure in each trial. Attracting cues (“EXIT” signs, audio instructions) and repelling cues (“DO NOT ENTER” signs, traffic cones) were placed in front of the two exits, either individually or in combination. In total, 2,125 datapoints were recorded from 20 participants. To model exit choice, machine learning (random forest, RF) models were applied to predict and interpret the guidance on evacuation choices. The tuned-hyperparameters RF model proposed in this study showed above 75% accuracy to predict evacuation choices facing conflict cues and was superior to default RF and logistic regression models. Interestingly, repelling cues such as “DO NOT ENTER” signs had a stronger impact on exit choice than attracting cues like “EXIT” signs when people have to make choices. Overall, the study offers valuable data and insights into exit choices, revealing that negative cues are more influential than positive ones in emergencies. These findings can significantly inform the design and optimization of egress guidance systems. This bias towards negative information under pressure suggests that evacuation systems should prioritize clear and prominent negative cues to guide occupants effectively. 

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