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Extreme-Density Crowd Simulation: Combining Agents with Smoothed Particle Hydrodynamics

Wouter van Toll 1 Cédric Braga 1 Barbara Solenthaler 2 Julien Pettré 1
1 RAINBOW - Sensor-based and interactive robotics
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities. In this paper, we combine agent-based crowd simulation with the concept of Smoothed Particle Hydrodynamics (SPH), a particle-based method that is popular for fluid simulation. Our combination augments the usual agent-collision handling with fluid dynamics when the crowd density is sufficiently high. A novel component of our method is a dynamic rest density per agent, which intuitively controls the crowd density that an agent is willing to accept. Experiments show that SPH improves agent-based simulation in several ways: better stability at high densities, more intuitive control over the crowd density, and easier replication of wave-propagation effects. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities.
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https://hal.inria.fr/hal-02964064
Contributor : Wouter van Toll <>
Submitted on : Monday, October 12, 2020 - 10:13:25 AM
Last modification on : Wednesday, October 14, 2020 - 3:12:19 AM

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Wouter van Toll, Cédric Braga, Barbara Solenthaler, Julien Pettré. Extreme-Density Crowd Simulation: Combining Agents with Smoothed Particle Hydrodynamics. 13th ACM SIGGRAPH Conference on Motion, Interaction and Games, Oct 2020, Virtual Event, United States. ⟨10.1145/3424636.3426896⟩. ⟨hal-02964064⟩

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