Multi-layered framework for crowd microscopic simulation

We address the challenging open problem of simulating virtual pedestrian crowd behaviors in real time inside virtual environment, in this context; we propose an overall architecture for defining the natural structure of crowd at three levels, the individual agent, group behaviors, and the relationships among individual agents and groups. Each level has its own complexity. The purpose of this paper is to create a model that try to cover all or most natural behaviors, so to do this, we propose a microscopic solution based on multi-layered architecture consisting of three level which together model the emergent crowd behaviors; the first layer, the physical layer realizes the perception and motion processes, the behavioral layer formulates the pedestrian behavior in order to follow his path together a goal, and finally the navigational layer which is responsible to implement the autonomous navigation process.

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

[2]  Abdullah Zawawi Talib,et al.  Modeling Groups of Pedestrians in Least Effort Crowd Movements Using Cellular Automata , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

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

[4]  Michael Lees,et al.  Analysis of an efficient rule-based motion planning system for simulating human crowds , 2010, The Visual Computer.

[5]  Demetri Terzopoulos,et al.  Animating autonomous pedestrians , 2005, SIGGRAPH '05.

[6]  Mark H. Overmars,et al.  Simulating the local behaviour of small pedestrian groups , 2010, VRST '10.

[7]  Michael Gleicher,et al.  Scalable behaviors for crowd simulation , 2004, Comput. Graph. Forum.

[8]  Hao Jiang,et al.  A semantic environment model for crowd simulation in multilayered complex environment , 2009, VRST '09.

[9]  Harry Gifford Crowd Simulation , 2013 .

[10]  Feng Li,et al.  A Cellular Automata Based Crowd Behavior Model , 2010, AICI.

[11]  Norman I. Badler,et al.  Virtual Crowds: Methods, Simulation, and Control , 2008, Virtual Crowds: Methods, Simulation, and Control.

[12]  Daniel Thalmann,et al.  Hierarchical Model for Real Time Simulation of Virtual Human Crowds , 2001, IEEE Trans. Vis. Comput. Graph..

[13]  Soraia Raupp Musse,et al.  Modeling individual behaviors in crowd simulation , 2003, Proceedings 11th IEEE International Workshop on Program Comprehension.

[14]  D. Thalmann,et al.  A navigation graph for real-time crowd animation on multilayered and uneven terrain , 2005 .

[15]  Daniel Thalmann,et al.  Real-Time Scalable Motion Planning for Crowds , 2007, 2007 International Conference on Cyberworlds (CW'07).

[16]  F. Cherif,et al.  Crowd simulation influenced by agent's socio-psychological state , 2010, ArXiv.

[17]  N. Badler,et al.  Crowd simulation incorporating agent psychological models, roles and communication , 2005 .

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

[19]  Demetri Terzopoulos,et al.  Autonomous pedestrians , 2005, SCA '05.

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