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

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.

[1]  L. Lucy A numerical approach to the testing of the fission hypothesis. , 1977 .

[2]  Angel Garcimartín,et al.  Flow of pedestrians through narrow doors with different competitiveness , 2016 .

[3]  Joaquim B. Cavalcante Neto,et al.  Gradient‐based steering for vision‐based crowd simulation algorithms , 2017, Comput. Graph. Forum.

[4]  J. Monaghan,et al.  Smoothed particle hydrodynamics: Theory and application to non-spherical stars , 1977 .

[5]  J. Silverberg,et al.  Can high-density human collective motion be forecasted by spatiotemporal fluctuations? , 2018, 1809.07875.

[6]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[7]  Gabriel A. Wainer,et al.  Context-sensitive Personal Space for Dense Crowd Simulation , 2017 .

[8]  Julien Pettré,et al.  Generalized Microscropic Crowd Simulation using Costs in Velocity Space , 2020, I3D.

[9]  Dinesh Manocha,et al.  Velocity-based modeling of physical interactions in multi-agent simulations , 2013, SCA '13.

[10]  J. Monaghan Smoothed particle hydrodynamics , 2005 .

[11]  Kalle Sjöström,et al.  Computational Fluid Dynamics in 2D Game Environments , 2011 .

[12]  Norman I. Badler,et al.  Controlling individual agents in high-density crowd simulation , 2007, SCA '07.

[13]  Pizzanu Kanongchaiyos,et al.  A Crowd Simulation Using Individual-Knowledge- Merge based Path Construction and Smoothed Particle Hydrodynamics , 2007 .

[14]  Ioannis Karamouzas,et al.  Universal power law governing pedestrian interactions. , 2014, Physical review letters.

[15]  Dinesh Manocha,et al.  Menge: A Modular Framework for Simulating Crowd Movement , 2016 .

[16]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[17]  André Borrmann,et al.  MomenTUMv2: A Modular, Extensible, and Generic Agent-Based Pedestrian Behavior Simulation Framework , 2016 .

[18]  Daniel Thalmann,et al.  Torso Crowds , 2017, IEEE Transactions on Visualization and Computer Graphics.

[19]  Adrien Treuille,et al.  Continuum crowds , 2006, SIGGRAPH 2006.

[20]  Chenfanfu Jiang,et al.  Position-based multi-agent dynamics for real-time crowd simulation , 2017, Symposium on Computer Animation.

[21]  Jan Bender,et al.  Volume Maps: An Implicit Boundary Representation for SPH , 2019, MIG.

[22]  Serge P. Hoogendoorn,et al.  Macroscopic pedestrian flow simulation using Smoothed Particle Hydrodynamics (SPH) , 2020, Transportation Research Part C: Emerging Technologies.

[23]  Ming C. Lin,et al.  Aggregate dynamics for dense crowd simulation , 2009, ACM Trans. Graph..

[24]  F. Santambrogio,et al.  A MACROSCOPIC CROWD MOTION MODEL OF GRADIENT FLOW TYPE , 2010, 1002.0686.

[25]  R. Hughes The flow of human crowds , 2003 .

[26]  Ming C. Lin,et al.  Continuum modeling of crowd turbulence. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Nuria Pelechano,et al.  Simulating Heterogeneous Crowds with Interactive Behaviors , 2014, Eurographics.

[28]  Roland Geraerts,et al.  Towards Believable Crowds : A Generic Multi-Level Framework for Agent Navigation , 2015 .

[29]  Markus H. Gross,et al.  Particle-based fluid simulation for interactive applications , 2003, SCA '03.

[30]  Dinesh Manocha,et al.  PLEdestrians: a least-effort approach to crowd simulation , 2010, SCA '10.

[31]  Jan Bender,et al.  Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids , 2020, Eurographics.

[32]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[33]  Dinesh Manocha,et al.  Velocity-based modeling of physical interactions in dense crowds , 2015, The Visual Computer.