Efficient Computational Models for Simulating Large-Scale, Heterogeneous Crowds
Abstract
Understanding the behavior of pedestrians in a crowded scene has been the subject of extensive research in multiple domains, including applied mathematics, robotics, psychology, sociology, civil and traffic engineering, architectural and urban design, etc. Many applications such as training for battlefield simulation and urban warfare, intelligent surveillance, management of large mobs or unruly crowds, as well as use of robots in battlefields and dangerous environments need improved capabilities to simulate large crowds. Such crowds are characterized based on number of agents or pedestrians (e.g. large crowds with tens or hundreds of thousands of people), high densities, as well as heterogeneous or varying behaviors. Current state of the art is not able to model such large and diverse crowds that arise in different applications. Motivated by the practical demands of modeling and simulation and better understanding of dynamic aggregate behaviors that are observed in modern-day Megacities, we propose to develop novel computational models and real-time crowd simulation algorithms that can be used to for battlefield simulation, personnel training, and design evaluation. The underlying challenges include: Accurate pedestrian geometrical models based on elliptical shapes and bio-mechanical constraints; Modeling of intricate pedestrian dynamics on uneven terrains; Interactive simulation and visualization of crowd flows using continuum formulation; Innovative hybrid combination of discrete-continuum crowd models; Novel data-driven crowd simulation algorithms using Bayesian learning that exploit the behaviors captured using videos and sensor data; Real-time simulation algorithms that exploit the parallel capabilities of multi-core CPUs and GPUs; Applications of these algorithms for architectural and urban design, virtual-reality training, planning of emergency response, etc.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 04, 2018
- Source ID
- W911NF1610085
Entities
People
- Ming C. Lin
Organizations
- Army Contracting Command
- United States Army
- University of North Carolina at Chapel Hill