Controlling Individual Agents in
High Density Crowd Simulation
N. Pelechano, J.M. Allbeck and N.I. Badler (2007)
Outline
Introduction
 Related Work
 The Model
 Results
 Conclusions
 Assesments

The Authors

N. Pelechano
◦ Assoc. Prof. at Catalunya University.
◦ Crowd simulation, real-time 3D, human-avatar
interactions

J.M. Allbeck
◦ Assist. Prof. at George Mason University.
◦ Animation, AI, physcology in crowds

N.I. Badler
◦ Professor at University of Pennsylvania
◦ Computational connections between language and
action
Introduction

A model for High Density Autonomous
Crowds (HiDAC)
◦ Natural, realistic crowd simulation
◦ Handle high density
◦ Adapt to dynamic changes
Introduction
Hybrid approach
 Physical forces with rules:

◦ Physiological (locomotion)
◦ Psychological (personality, panic..)
◦ Geometrical (distance, angles..)

Two levels:
◦ High level – global
◦ Low level – local
Related Work

Helbing’s Social Forces model
◦ Particle simulations , Oscillations
◦ Extensions exist – real-time problems

Rule-based models, i.e. Reynold’s
◦ Realistic, for low-medium density
◦ Avoid individual contacts
Related Work

Cellular Automota models
◦ No contact between agents

Higher level navigation
◦ Roadmaps
◦ Potential Fields
◦ Cell and portal graphs
Related Work
The Model - Overview
High Level Module
Modeling Crowd and Trained Leader Behavior during Building Evacuation,
by Pelechano and Badler. (2006)
Low Level Module
Prevent oscillations
 Create bi-directional flows
 Queueing
 Pushing
 Agents falling and act as obstacles
 Propogate panic
 Exhibit impatience
 React to dynamic changes

Low Level Module

Movement of an agent
Low Level Module

Then, position is:
◦
◦
◦
◦
◦
α : agent will move or be pushed
v : velocity ( <= Vmax), constant a
β : priority value to avoid fallen agents
r : result of repulsive forces
T : time step
Forces: Avoidance
Forces:Avoidance
• D : viewing rectangle
•Increase/decrease based on density
• Weights:
• d: distance between agents
• o: orientation of velocity vector
Forces: Avoidance

Bi-directional flows with right preference and
altering rectangle of influence
Forces: Repulsion
•λ : Priority value between agents and
walls/obstacles
• Walls > Agents
Shaking Problem

Stop moving if:
◦ Agent is not in panic
◦ Repulsion against the agent

Can still be pushed forward.
Waiting Behaviour
Allows queueing
 Disk of influence

◦ Depends on desired behaviour
Pushing Behaviour

Personal space (disk)
◦ I.e. Low for impatient agent

Apply collision response force
Falling Agents

When pushing forces are high

Becomes an obstacle

No repulsive force
Panic Propagation

High-level module
◦ Communication between agents

Low-level module
◦ Agent detects density or pushing
Dynamic changes and bottlenecks

High-level module
◦ Supply alternative paths
Results

85 room environment

Simulation only:
◦ 25 fps
◦ 1800 characters

Simulation and 3D rendering
◦ 25 fps
◦ 600 simple 3d human figures
Conclusions

Ability to simulate low-high density
◦ Panic and calm situations

New and natural behaviours
◦ Pushing, queueing, falling agents...

User needs to define parameters for
different environments/situations
Assesments – The paper

Local methods/behaviours
◦ Clear explanation
◦ Supported with figures and results

Experiments & Results
◦ Rather scattered
◦ One or few comparative tests
◦ Could be more
Assesments – The method

No problems with the model?

Behaviours and the model depend also on
high-level module
◦ Limited adaptability
◦ Gaps in the method explanation
Assesments – The method

Performance
◦ 25 fps, 600 human figures
◦ Enough for simulations and/or games?

Applicability
◦ Rather limited
◦ Would serve for industrial applications
Assesments – The method
Incorporate global and local approach
 Natural in high density

◦ Individual contacts/interactions

Globay wayfinding
◦ Shortest path
◦ Maybe deliver another approach
 Roadmaps, corridor maps
Assesments – The method

Lacks prediction/anticipation
◦ A Predictive Collision Avoidance Model for Pedestrian
Simulation, Karamouzas et al.(2009)

Able to handle high density
◦ Morphable Crowds, Eunjung Ju et al. (2010)

Integration of a personality model
◦ How the Ocean Personality Model Affects the Perception of
Crowds, F. Durupinar et al. ( 2011)