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arXiv · 2306.17824

Learning Evacuee Models from Robot-Guided Emergency Evacuation Experiments

Mollik Nayyar·Ghanghoon Paik·Zhenyuan Yuan·Tongjia Zheng·Minghui Zhu·Hai Lin·Alan R. Wagner
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Abstract

Recent research has examined the possibility of using robots to guide evacuees to safe exits during emergencies. Yet, there are many factors that can impact a person's decision to follow a robot. Being able to model how an evacuee follows an emergency robot guide could be crucial for designing robots that effectively guide evacuees during an emergency. This paper presents a method for developing realistic and predictive human evacuee models from physical human evacuation experiments. The paper analyzes the behavior of 14 human subjects during physical robot-guided evacuation. We then use the video data to create evacuee motion models that predict the person's future positions during the emergency. Finally, we validate the resulting models by running a k-fold cross-validation on the data collected during physical human subject experiments. We also present performance results of the model using data from a similar simulated emergency evacuation experiment demonstrating that these models can serve as a tool to predict evacuee behavior in novel evacuation simulations.

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Mollik Nayyar, Ghanghoon Paik, Zhenyuan Yuan, Tongjia Zheng, Minghui Zhu, Hai Lin, Alan R. Wagner. 2023-06-30. Learning Evacuee Models from Robot-Guided Emergency Evacuation Experiments. https://arxiv.org/abs/2306.17824

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