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Controlling Individual Agents in High Density Crowd Simulation N. Pelechano, J.M. Allbeck and N.I. Badler (2007)

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Presentation on theme: "Controlling Individual Agents in High Density Crowd Simulation N. Pelechano, J.M. Allbeck and N.I. Badler (2007)"— Presentation transcript:

1 Controlling Individual Agents in High Density Crowd Simulation N. Pelechano, J.M. Allbeck and N.I. Badler (2007)

2 Outline Introduction Related Work The Model Results Conclusions Assesments

3 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

4 Introduction A model for High Density Autonomous Crowds (HiDAC) ◦ Natural, realistic crowd simulation ◦ Handle high density ◦ Adapt to dynamic changes

5 Introduction Hybrid approach Physical forces with rules: ◦ Physiological (locomotion) ◦ Psychological (personality, panic..) ◦ Geometrical (distance, angles..) Two levels: ◦ High level – global ◦ Low level – local

6 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

7 Related Work Cellular Automota models ◦ No contact between agents Higher level navigation ◦ Roadmaps ◦ Potential Fields ◦ Cell and portal graphs

8 Related Work

9 The Model - Overview

10 High Level Module Modeling Crowd and Trained Leader Behavior during Building Evacuation, by Pelechano and Badler. (2006)

11 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

12 Low Level Module Movement of an agent

13 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

14 Forces: Avoidance

15 Forces:Avoidance D : viewing rectangle Increase/decrease based on density Weights: d: distance between agents o: orientation of velocity vector

16 Forces: Avoidance Bi-directional flows with right preference and altering rectangle of influence

17 Forces: Repulsion λ : Priority value between agents and walls/obstacles Walls > Agents

18 Shaking Problem Stop moving if: ◦ Agent is not in panic ◦ Repulsion against the agent Can still be pushed forward.

19 Waiting Behaviour Allows queueing Disk of influence ◦ Depends on desired behaviour

20 Pushing Behaviour Personal space (disk) ◦ I.e. Low for impatient agent Apply collision response force

21 Falling Agents When pushing forces are high Becomes an obstacle No repulsive force

22 Panic Propagation High-level module ◦ Communication between agents Low-level module ◦ Agent detects density or pushing

23 Dynamic changes and bottlenecks High-level module ◦ Supply alternative paths

24 Results 85 room environment Simulation only: ◦ 25 fps ◦ 1800 characters Simulation and 3D rendering ◦ 25 fps ◦ 600 simple 3d human figures

25 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

26 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

27 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

28 Assesments – The method Performance ◦ 25 fps, 600 human figures ◦ Enough for simulations and/or games? Applicability ◦ Rather limited ◦ Would serve for industrial applications

29 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

30 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)

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