Abstract

Many societal activities, including air transport, disaster remediation, social events such as concerts and sports, require efficient and effective methodologies for monitoring, understanding, and reacting to behaviors of large concentrations of people, the crowds, that give rise to those events. Simultaneously, the type and evolution of those behaviors are intimately tied to the form and function of the environments where they occur. As crowds increase in size or change their actions in response to intrinsic or extrinsic factors, it is critical for the built environments, including their future designs, to adapt to those changes. Present-day technological tools aim to analyze and predict the link between crowds and environments. However, they rely on rigid, hand-tuned, computationally costly simulation models, severely limiting their practical utility. This project seeks to bridge this gap by devising a novel way of modeling the inherent relationship between the structure and semantics of complex environments, and the presence and behavior of its human occupants, from small groups to dense crowds. The main goal is to predict crowd behavior accurately, from microscopic motion to aggregate crowd dynamics, in novel, never-before-seen environment configurations using Neuro-Cognitive Modeling of Environments and Humans (NUCLEUM) to replace the computationally expensive yet often mismatched-with-reality physical simulations.

Events

Annual Summer Workshop on Artificial Intelligence - Special Topic on Human, Crowd, Environment and Robotics
Rutgers University / The College of New Jersey, 2021-2024.
Information Page

Neuro-Cognitive Modeling of Humans and Environments (NCMHE)
International Joint Conference on Artificial Intelligence (IJCAI) Workshop, 2021.
Workshop Page

Selected Publications

	Samuel S. Sohn, et al. Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction International Joint Conference on Artificial Intelligence (IJCAI), 2022. Project Page

	Mihee Lee, et al. MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction Computer Vision and Pattern Reconition (CVPR), 2022. Project Page

	Honglu Zhou, et al. Hopper: Multi-hop Transformer for Spatiotemporal Reasoning International Conference on Learning Representations (ICLR), 2021. Project Page

	Samuel S. Sohn, et al. A2X: An Agent and Environment Interaction Benchmark for Multimodal Human Trajectory Prediction Motion, Interaction and Games (MIG), 2021. Project Page

	Samuel S. Sohn, et al. Laying the Foundations of Deep Long-Term Crowd Flow Prediction European Conference on Computer Vision (ECCV), 2020. Project Page

Acknowledgements

This project is supported by National Science Foundation Grant Awards #1955404 and #1955365.