Simulating Behavior Diversity in BioCrowds
Resumo
Most of the techniques available nowadays for crowd simulation are focused on a specific situation, e.g. evacuation inhazardous events. Very few of them consider the cultural and personality aspects present in a society to determine the behavior of agents. Therefore, this work aims to build a framework able to deal with different cultural and personality traits as input, and translate them into a group parametrization, which is going to determine the behavior of groups and crowds in virtual environments. Also, we include in BioCrowds a comfort response for agents, in terms of density and thermal characteristics of the environment. Results indicate that the cultural/psychological mappings seem promising, since agents were able to perform as intended. Additionally, agents were able to react due to thermal and density comfort, improving their ability to react to environmental changes.
Referências
D. Helbing and P. Molnar, “Social force model for pedestrian dynamics,” Physical Review E, vol. 51, no. 5, pp. 4282–4286, May 1995. https://doi.org/10.1103/physreve.51.4282
J. J. Fruin, “Pedestrian planning and design,” Metro Asso Urban Dsgners & Envir Plners, Tech. Rep., 1971, 206p.
A. Runions, M. Fuhrer, B. Lane, P. Federl, A.-G. Rolland-Lagan, and P. Prusinkiewicz, “Modeling and visualization of leaf venation patterns,” ACM Transactions on Graphics, vol. 24, no. 3, pp. 702–711, Jul 2005. https://doi.org/10.1145/1073204.1073251
J. Van Den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in Proceedings of the Robotics Research, 2011, pp. 3–19. https://doi.org/10.1007/978-3-642-19457-3_1
D. Lala, S. Thovuttikul, and T. Nishida, “Towards a virtual environment for capturing behavior in cultural crowds,” in Proceedings of the 6th International Conference on Digital Information Management. IEEE, 2011, pp. 310–315. https://doi.org/10.1109/ICDIM.2011.6093362
G. H. Hofstede, Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. USA: Sage, 2001, 616p.
F. Durupınar, U. Güdükbay, A. Aman, and N. I. Badler, “Psychological parameters for crowd simulation: From audiences to mobs,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 9, pp. 2145–2159, Sep 2016. https://doi.org/10.1109/TVCG.2015.2501801
C. W. Reynolds, “Flocks, herds and schools: A distributed behavioral model,” in Proceedings of the ACM Special Interest Group on Computer Graphics and Interactive Techniques. ACM, 1987, pp. 25–34. https://doi.org/10.1145/37401.37406
L. R. Goldberg, “An alternative description of personality: the big-five factor structure.” Journal of Personality and Social Psychology, vol. 59, no. 6, pp. 1216–1229, Jun 1990. https://doi.org/10.1037//0022-3514.59.6.1216
L. Chen, C. R. Jung, S. R. Musse, M. Moneimne, C. Wang, R. Fruchter, V. Bazjanac, G. Chen, and N. I. Badler, “Crowd simulation incorporating thermal environments and responsive behaviors,” Presence Journal, in press. https://doi.org/10.1162/PRES_a_00308
P. O. Fanger et al., Thermal comfort. Analysis and applications in environmental engineering. DK: Danish Technical Press, 1970, 244p.
R. M. Favaretto, L. Dihl, R. Barreto, and S. R. Musse, “Using group behaviors to detect hofstede cultural dimensions,” in Proceedings of the 2016 IEEE International Conference on Image Processing. IEEE, 2016, pp. 2936–2940. https://doi.org/10.1109/ICIP.2016.7532897
E. Hall, “The hidden dimension, vol. 1990,” USA: Anchor Books, 1969, 240p.
