Abstract
The processing time to simulate crowds for games or simulations is a real challenge. While the increasing power of processing capacity is a reality in the hardware industry, it also means that more agents, better rendering and most sophisticated Artificial Intelligence (AI) methods can be used, so again the computational time is an issue. Despite the processing cost, in many cases the most interesting period of time in a game or simulation is far from the beginning or in a specific known period, but it is still necessary to simulate the whole time (spending time and processing capacity) to achieve the desired period of time. It would be useful to fast forward the time in order to see a specific period of time where simulation result could be more meaningful for analysis. This paper presents a method to provide time travel in Crowd Simulation. Based on crowd features, we compute the expected variation in velocities and apply that for time travel in crowd simulation.
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Notes
- 1.
This scenario could not be simulated with 320 agents because the agents were stuck due to lack of free space.
References
Adams, E.: Fundamentals of Game Design. New Riders Press, Berkeley (2014)
Beauregard, S., Haas, H.: Pedestrian dead reckoning: a basis for personal positioning. In: Proceedings of the 3rd Workshop on Positioning, Navigation and Communication (WPNC06), p. 1 (2006)
Bicho, A.L., Rodrigues, R.A., Musse, S.R., Jung, C.R., Paravisi, M., Magalhes, L.P.: Simulating crowds based on a space colonization algorithm. Comput. Graph. 36(2), 70–79 (2012). http://www.sciencedirect.com/science/article/pii/S0097849311001713. Virtual Reality in Brazil 2011
Bianco, C.M.D., Jovani Oliveira Brasil, A.B., Musse, S.R.: A model to compute people disturbance in crowds. In: Poster at ACM Motion in Games, vol. 1 (2015)
Everett, A.: Time travel paradoxes, path integrals, and the many worlds interpretation of quantum mechanics. Phys. Rev. D69, 124023. http://link.aps.org/doi/10.1103/PhysRevD.69.124023
Guy, S.J., Chhugani, J., Kim, C., Satish, N., Lin, M.C., Manocha, D., Dubey, P.: Clearpath: highly parallel collision avoidance for multi-agent simulation. In: ACM SIGGRAPH/EUROGRAPHICS Symposium on Computer Animation, pp. 177–187. ACM (2009)
Hagelback, J., Johansson, S.: Dealing with fog of war in a real time strategy game environment. In: IEEE Symposium On Computational Intelligence and Games, CIG 2008, pp. 55–62, December 2008
Hakiri, A., Berthou, P., Gayraud, T.: QoS-enabled anfis dead reckoning algorithm for distributed interactive simulation. In: Turner, S.J., Roberts, D.J. (eds.) DS-RT, pp. 33–42. IEEE Computer Society (2010). http://dblp.uni-trier.de/db/conf/dsrt/dsrt2010.html#HakiriBG10
Ladetto, Q., Merminod, B.: Digital magnetic compass and gyroscope integration for pedestrian navigation. In: 9th International Conference on Integrated Navigation Systems, St-Petersburg, pp. 27–29 (2002)
Osborne, D., Dickinson, P.: Improving Games AI Performance Using Grouped Hierarchical Level of Detail. Elsevier Science Inc., New York
Pettre, J., de Heras Ciechomski, P., Maim, J., Yersin, B., Laumond, J.P., Thalmann, D.: Real-time navigating crowds: scalable simulation and rendering. Comput. Anim. Virtual Worlds 17(3–4), 445–455 (2006). http://dx.doi.org/10.1002/cav.147
Pettr, J., Ondrej, J., Olivier, A.H., Crtual, A., Donikian, S.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Fellner, D.W., Spencer, S.N. (eds.) Symposium on Computer Animation, pp. 189–198. ACM (2009). http://dblp.uni-trier.de/db/conf/sca/sca2009.html#PettreOOCD09
Randell, C., Djiallis, C., Muller, H.: Personal position measurement using dead reckoning. In: Proceedings of The Seventh International Symposium on Wearable Computers, pp. 166–173. Springer (2003)
Runions, A., Fuhrer, M., Lane, B., Federl, P., Rolland-Lagan, A.G., Prusinkiewicz, P.: Modeling and visualization of leaf venation patterns. In: ACM SIGGRAPH 2005 Papers, SIGGRAPH 2005, NY, USA, pp. 702–711 (2005). http://doi.acm.org/10.1145/1186822.1073251
Capin, T.K., Pandzic, I.S., Thalmann, N.M., Thalmann, D.: A dead-reckoning algorithm for virtual human figures. Proceedings of VRAIS97 1(1), 161–169 (1997)
Zampella, F., Ruiz, A.R.J., Granja, F.S.: Indoor positioning using efficient map matching, RSS measurements, and an improved motion model. IEEE Trans. Veh. Technol. 64(4), 1304–1317 (2015)
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Bianco, C.M.D., Braun, A., Musse, S.R., Jung, C., Badler, N. (2016). Fast-Forwarding Crowd Simulations. In: Traum, D., Swartout, W., Khooshabeh, P., Kopp, S., Scherer, S., Leuski, A. (eds) Intelligent Virtual Agents. IVA 2016. Lecture Notes in Computer Science(), vol 10011. Springer, Cham. https://doi.org/10.1007/978-3-319-47665-0_19
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