Sound and smoke propagation models for virtual crowd environments

Virtual crowd simulations are important for various applications like defense, social studies, etc. While literature exists on agent design and sensor modeling, there is still no generic model for sound and smoke propagation that can be integrated into existing virtual crowd simulation frameworks. T...

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Bibliographic Details
Main Author: Vaisagh Viswanathan Thattamparambil
Other Authors: Cai Wentong
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/38571
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Institution: Nanyang Technological University
Language: English
Description
Summary:Virtual crowd simulations are important for various applications like defense, social studies, etc. While literature exists on agent design and sensor modeling, there is still no generic model for sound and smoke propagation that can be integrated into existing virtual crowd simulation frameworks. This project attempts to address this issue by creating models for sound and smoke propagation in areas like MRT stations and parade grounds. It begins with an introduction to the basics of sound and smoke and a survey of the background and existing work in the fields of intelligent agents, virtual crowds and sensory and perception models. The concept of cellular automata and the application of the finite difference algorithm to a cellular automata is also discussed. The project then moves on to explain the characteristic features and properties of sound and smoke and also the constraints involved in modeling such a propagation model for virtual crowd environments. The proposed models for smoke and sound are then explained in detail and their performance in an agent based virtual crowd simulation is examined. The sound model suggested in this project could be further enhanced by adding additional capabilities like reflection and echoes for sound. The smoke model can be improved by improving the performance and making it smoother and less memory intensive to run. Work also needs to be done to model the perception capabilities of agents in a realistic way.