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Modelling and Simulation of Pedestrian Behaviours
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Source Workshop on Parallel and Distributed Simulation archive
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation table of contents
Pages 43-50  
Year of Publication: 2008
ISBN ~ ISSN:1087-4097 , 978-0-7695-3159-5
Authors
Publisher
IEEE Computer Society  Washington, DC, USA
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Downloads (6 Weeks): 23,   Downloads (12 Months): 130,   Citation Count: 0
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DOI Bookmark: 10.1109/PADS.2008.27

ABSTRACT

The modelling and simulation of autonomous pedestrians has important applications in real-time crowd and crisis simulations. With the increase in processing powers and dedicated graphics cards, more processing powers can now be allocated for the generation of realistic behaviours for individuals within the crowd. We have proposed a representation for autonomous agents that is aimed to generate some human-like behaviours. In particular, to generate smooth and realistic navigational behaviour such as human-like collision avoidance, we have also proposed a two tier navigation model for autonomous agents. Using the proposed model, a scene in a typical shopping mall has been created. Behaviours such as crowd avoidance and lane following have been observed from the simulation.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
V. J. Blue and J. L. Adler. Cellular automata microsimulation for modelling bi-directional pedestrian walkways. Transportaion Research Part B, 35:293-312, 2001.
 
2
 
3
M. P. Bryden. Attentional strategies and short-term memory in dichotic listening. Cognitive Psychology, 2:99-116, 1971.
 
4
 
5
D. Eberly. 3D Game Engine Design: A Pracitcal Approach to Real-Time Computer Graphics, 2nd Edition. Morgan Kaufmann Publishers, 2007.
 
6
P. E. Hart, N. J. Nilsson, and B. Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics SSC4, 2:100107, 1968.
 
7
D. Helbing. Collective phenomena and states in traffic and self-driven many particles systems. Computation Material Science, 30:180-187, 2004.
 
8
D. Helbing and P. Molnár. Social force model for pedestrian dynamics. Physical Review, 51:4282-4286, 1995.
 
9
S. P. Hoogendoorn and P. H. L. Bovy. Generic gas-kinetic traffic systems modeling with applications to vehicular traffic flow. Transportation Research Part B, 35:317-336, 2001.
 
10
 
11
C. Miles and S. J. Louis. Towards the co-evolution of influence map tree based strategy game players. In Proceedings of IEEE Symposium on Computational Intelligence and Games 2006, pages 75-82, Reno, NV, U.S.A., 2006.
 
12
 
13
T. T. Pires. An approach for modelling human cognitive behaviour in evacuation models. Fire Safety Journal, 40:177- 189, 2005.
 
14
M. I. Posner, C. R. R. Snyder, and D. J. Davidson. Attention and the detection of signals. Journal of Experimental Psychology: General, 109:160-174, 1980.
 
15
 
16
C. W. Reynolds. Steering behaviours for autonomous characters. In Proceedings of Game Developer Conference, pages 763-782, San Francisco, CA, USA, 1999.
 
17
 
18
G. Snook. Simplified 3d movement and path-finding using navigation meshes. Game Programming Gems, 2000.
19
 
20
L. N. Vaserstien. Introduction to Linear Programming. Prentice Hall, 2003.

Collaborative Colleagues:
Wee Lit Koh: colleagues
Lin Lin: colleagues
Suiping Zhou: colleagues