Learning Evacuee Models from Robot-Guided Emergency Evacuation Experiments
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.
Cite
@article{arxiv.2306.17824,
title = {Learning Evacuee Models from Robot-Guided Emergency Evacuation Experiments},
author = {Mollik Nayyar and Ghanghoon Paik and Zhenyuan Yuan and Tongjia Zheng and Minghui Zhu and Hai Lin and Alan R. Wagner},
journal= {arXiv preprint arXiv:2306.17824},
year = {2023}
}