Individual-Knowledge-Merge for Crowd Simulation

1. A Crowd Simulation Using Individual-Knowledge-Merge based Path Construction and Smoothed Particle Hydrodynamics Weerawat Tantisiriwat, Arisara Sumleeon and Pizzanu Kanongchaiyos Dept. of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand.

2. Outline • Introduction • Literature Review • Objective • Individual-Knowledge-Merge Method • Conclusion & Future Works

3. Outline • Introduction • Literature Review • Objective • Individual-Knowledge-Merge Method • Conclusion & Future Works

4. Introduction • The current crowd simulation consist of 2 steps First : Prepare the global path construction to go to the destination. Second : Simulate the crowd locomotion along with the created path by using behavioral rules.

5. Outline • Introduction • Literature Review • Objective • Individual-Knowledge-Merge Method • Conclusion & Future Works

6. Related Works(1/4) Autonomous pedestrians [Wei et al., 2005] • Able to analyzethe situation from the perceiving environment. • Unable to demonstrate natural crowd locomotion.

7. Related Works(2/4) Continuum Crowds [Treuille et al., 2006] • Able to demonstrate unfixed-pattern of crowd locomotion. • Unable to analyze the situation from surrounding environment. • Unable to automatically find the destination.

8. Related Works(3/4) Continuum Crowds [Treuille et al., 2006] • Able to generate the locomotion direction in the all position. • Able to avoid the obstacle and another individual automatically. • Unable to generate potential if do not specify the destination.

9. Related Works(4/4) • The results : • Interactive time simulation. • Natural phenomena locomotion demonstration. • The problems : • Unable to simulate crowd behavior for finding the destination in unknown environment. • Unable to construct the path if do not use the global map knowledge.

10. Outline • Introduction • Literature Review • Objective • Individual-Knowledge-Merge Method • Conclusion & Future Works

11. Objective • To simulate crowd behavior for finding the destination in the unknown environment. • To simulate unfixed-pattern of crowd locomotion.

12. Outline • Introduction • Literature Review • Objective • Individual-Knowledge-Merge Method • Conclusion & Future Works

13. Implementation

14. Individual-Knowledge-Merge(1/9) This methodis used to simulate crowd behavior and crowd locomotion for finding the destination in unknown environment by using local map knowledge. Consist of : - Perception - Recognition - Decision - Locomotion

15. - Vision - Communication Individual-Knowledge-Merge(2/9) Perception To perceive the data in the environment by shooting a ray.

16. Individual-Knowledge-Merge(3/9) Recognition To create local map knowledge by recognizing from perception.

17. Individual-Knowledge-Merge(4/9) Decision To select a appropriate path from generated potential to go the destination. Case 1 :The destination is in the local map knowledge. Case 2 : The destination does not be in the local map knowledge.

18. = Distance = Density = Convenience Individual-Knowledge-Merge(5/9) Decision : The destination is in the map

19. Individual-Knowledge-Merge(6/9) Decision : The destination does not be in the map The connection area is became a minor destination

20. Individual-Knowledge-Merge(7/9) Locomotion To calculate next position by using computational fluid dynamics. Smooth Particle Hydrodynamics SPHs is an interpolation method that approximates the value of a continuous field quantity and its derivative by using discrete sample points. {

21. Pressure force Viscous force External body force = Potential force field Individual-Knowledge-Merge(8/9) Locomotion

22. Individual-Knowledge-Merge(9/9) Locomotion

23. Outline • Introduction • Literature Review • Objective • Individual-Knowledge-Merge Method • Conclusion & Future Works

24. Conclusion & Future Works Conclusion This system can use for crowd simulation by • Able to find the destination in the unknown environment automatically. • Able to demonstrate unfixed-pattern of crowd locomotion. Future Works • Improve the behavioral model. • Improve the decision factors.