Aggregate dynamics for dense crowd simulation

Narain, Rahul; Golas, Abhinav; Curtis, Sean; Lin, Ming C.

doi:10.1145/1618452.1618468

Cited by 218 publications

(80 citation statements)

References 39 publications

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“…It is important to note that these two weights are not independent. As has been noted by [2] and others, velocity variance decreases at high densities. Thus adding a weight for velocity variance does not affect high density behavior significantly.…”

Section: Hybrid Crowd Simulationsupporting

confidence: 77%

“…This simple approach produces excellent avoidance results with maximal progress while still being computationally inexpensive. Though similar in spirit to [2], it avoids the need to calculate a global pressure to exert forces.…”

Section: Continuum Lookaheadmentioning

confidence: 99%

“…Continuum algorithms (e.g. [2]) are ideally suited for medium to high densities, since the continuum assumption holds when pedestrian paths are tightly constrained by the nearby neighbors found at these levels of density. On the other hand, though discrete algorithms can be applied at any density, their computational costs escalate at high densities, along with numerical issues in some cases.…”

Section: Introductionmentioning

confidence: 99%

“…However, its computational cost increases steeply with the number of distinct goals in the simulation, making this approach unsuitable for interactive crowd simulation where agents may have many diverse goals. Narain et al [2] propose a technique that prevents overcrowding in highly dense crowds, but it relies on purely local information and thus cannot plan around congestion at a large distance.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Long-Range Collision Avoidance for Crowd Simulation

Golas

Narain

Curtis

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-Local collision avoidance algorithms in crowd simulation often ignore agents beyond a neighborhood of a certain size. This cutoff can result in sharp changes in trajectory when large groups of agents enter or exit these neighborhoods. In this work, we exploit the insight that exact collision avoidance is not necessary between agents at such large distances, and propose a novel algorithm for extending existing collision avoidance algorithms to perform approximate, long-range collision avoidance. Our formulation performs long-range collision avoidance for distant agent groups to efficiently compute trajectories that are smoother than those obtained with state-of-the-art techniques and at faster rates. Comparison to real-world data demonstrates that crowds simulated with our algorithm exhibit an improved speed sensitivity to density similar to human crowds. Another issue often sidestepped in existing work is that discrete and continuum collision avoidance algorithms have different regions of applicability. For example, low-density crowds cannot be modeled as a continuum, while high-density crowds can be expensive to model using discrete methods. We formulate a hybrid technique for crowd simulation which can accurately and efficiently simulate crowds at any density with seamless transitions between continuum and discrete representations. Our approach blends results from continuum and discrete algorithms, based on local density and velocity variance. In addition to being robust across a variety of group scenarios, it is also highly efficient, running at interactive rates for thousands of agents on portable systems.

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Section: Hybrid Crowd Simulationsupporting

confidence: 77%

Section: Continuum Lookaheadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Long-Range Collision Avoidance for Crowd Simulation

Golas

Narain

Curtis

et al. 2014

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

show abstract

“…One common method to the simulation of mass flow is to model pedestrian in a particlelike manner [6], [7]. To the degree that humans are physical entities, and behave like particles, e.g., forming vortices as they move through an aperture [8], these flow-based models have been able to account for certain crowd characteristics.…”

Section: A Crowd Dynamics Simulation Modelsmentioning

confidence: 99%

Modeling Agent Social Joint Actions via Micro and Macro Coordination Strategies

Park

Quek

Cao

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-This paper presents a multi-agent model for large crowd simulations that addresses the need for socially plausible coordination behavior. A computational model for multi-agent coordination informed by well-established common ground theory is proposed. We introduce the idea of macro-and microcoordination strategies that allow agent-based simulations to adapt to different domains. Our agent model allows the selection of appropriate behaviors based on the spatiotemporal conditions of the agent-group's environment. By showing that different micro-coordination strategies of individual groups has an influence on the overall distribution of a crowd, we demonstrate the importance of incorporating such models into multi-agent simulations of large crowd behaviors.

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Animation Research: Modern Techniques

Tao

2013

Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

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Aggregate dynamics for dense crowd simulation

Cited by 218 publications

References 39 publications

Hybrid Long-Range Collision Avoidance for Crowd Simulation

Hybrid Long-Range Collision Avoidance for Crowd Simulation

Modeling Agent Social Joint Actions via Micro and Macro Coordination Strategies

Animation Research: Modern Techniques

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