Abstract
Microscopic crowd simulators rely on models of local interaction (e.g. collision avoidance) to synthesize the individual motion of each virtual agent. The quality of the resulting motions heavily depends on this component, which has significantly improved in the past few years. Recent advances have been in particular due to the introduction of a short-horizon motion prediction strategy that enables anticipated motion adaptation during local interactions among agents. However, the simplicity of prediction techniques of existing models somewhat limits their domain of validity. In this paper, our key objective is to significantly improve the quality of simulations by expanding the applicable range of motion predictions. To this end, we present a novel local interaction algorithm with a new context-aware, probabilistic motion prediction model. By context-aware, we mean that this approach allows crowd simulators to account for many factors, such as the influence of environment layouts or in-progress interactions among agents, and has the ability to simultaneously maintain several possible alternate scenarios for future motions and to cope with uncertainties on sensing and other agent's motions. Technically, this model introduces "collision probability fields" between agents, efficiently computed through the cumulative application of Warp Operators on a source Intrinsic Field. We demonstrate how this model significantly improves the quality of simulated motions in challenging scenarios, such as dense crowds and complex environments.
Supplemental Material
Available for Download
Supplemental file.
- Chenney, S. 2004. Flow tiles. In Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer animation, Eurographics Association, Aire-la-Ville, Switzerland, 233--242. Google ScholarDigital Library
- Cividini, J., Appert-Rolland, C., and Hilhorst, H.-J. 2013. Diagonal patterns and chevron effect in intersecting traffic flows. EPL (Europhysics Letters) 102, 2, 20002.Google ScholarCross Ref
- Feurtey, F. 2000. Simulating the Collision Avoidance Behavior of Pedestrians. Master's thesis, Department of Electronic Engineering, University of Tokyo.Google Scholar
- Golas, A., Narain, R., and Lin, M. 2013. Hybrid long-range collision avoidance for crowd simulation. In Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, ACM, New York, NY, USA, I3D '13, 29--36. Google ScholarDigital Library
- Guy, S. J., Chhugani, J., Kim, C., Satish, N., Lin, M., Manocha, D., and Dubey, P. 2009. Clearpath: Highly parallel collision avoidance for multi-agent simulation. In Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 177--187. Google ScholarDigital Library
- Guy, S. J., Curtis, S., Lin, M. C., and Manocha, D. 2012. Least-effort trajectories lead to emergent crowd behaviors. Phys. Rev. E 85 (Jan), 016110.Google ScholarCross Ref
- Guy, S. J., van den Berg, J., Liu, W., Lau, R., Lin, M. C., and Manocha, D. 2012. A statistical similarity measure for aggregate crowd dynamics. ACM Trans. Graph. 31. Google ScholarDigital Library
- Helbing, D., and Molnár, P. 1995. Social force model for pedestrian dynamics. Physical Review E 51, 5, 4282--4286.Google ScholarCross Ref
- Helbing, D., Farkas, I., and Vicsek, T. 2000. Simulating dynamical features of escape panic. Nature 407, 6803, 487--490.Google Scholar
- Jin, X., Xu, J., Wang, C. C. L., Huang, S., and Zhang, J. 2008. Interactive control of large-crowd navigation in virtual environments using vector fields. IEEE Comput. Graph. Appl. 28, 6 (Nov.), 37--46. Google ScholarDigital Library
- Ju, E., Choi, M., Park, M., Lee, J., Lee, K., and Takahashi, S. 2010. Morphable crowds. ACM Trans. Graph. 29. Google ScholarDigital Library
- Kapadia, M., Singh, S., Hewlett, W., and Faloutsos, P. 2009. Egocentric affordance fields in pedestrian steering. In Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games, ACM, New York, NY, USA, I3D '09, 215--223. Google ScholarDigital Library
- Karamouzas, I., Heil, P., Beek, P., and Overmars, M. H. 2009. A predictive collision avoidance model for pedestrian simulation. In Proceedings of the 2nd International Workshop on Motion in Games, Springer-Verlag, Berlin, Heidelberg, 41--52. Google ScholarDigital Library
- Karamouzas, I., Skinner, B., and Guy, S. J. 2014. Universal power law governing pedestrian interactions. Phys. Rev. Lett. 113 (Dec), 238701.Google ScholarCross Ref
- Kendall, M. G. 1938. A new measure of rank correlation. Biometrika 30, 1/2, 81--93.Google Scholar
- Kim, S., Guy, S. J., Liu, W., Wilkie, D., Lau, R. W., Lin, M. C., and Manocha, D. 2014. Brvo: Predicting pedestrian trajectories using velocity-space reasoning. The International Journal of Robotics Research. Google ScholarDigital Library
- Kretz, T., and Schreckenberg, M. 2008. The f.a.s.t.-model. CoRR abs/0804.1893. Google ScholarDigital Library
- Lerner, A., Chrysanthou, Y., and Lischinski, D. 2007. Crowds by example. Computer Graphics Forum 26, 3, 655--664.Google ScholarCross Ref
- Liu, C. K., Hertzmann, A., and Popović, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. Graph. 24, 3 (July), 1071--1081. Google ScholarDigital Library
- Narain, R., Golas, A., Curtis, S., and Lin, M. C. 2009. Aggregate dynamics for dense crowd simulation. ACM Transactions on Graphics 28, 122:1--122:8. Google ScholarDigital Library
- Olivier, A.-H., Marin, A., Crétual, A., and Pettré, J. 2012. Minimal predicted distance: A common metric for collision avoidance during pairwise interactions between walkers. Gait & posture 36, 3, 399--404.Google Scholar
- Ondřej, J., Pettré, J., Olivier, A.-H., and Donikian, S. 2010. A synthetic-vision based steering approach for crowd simulation. ACM Trans. Graph. 29, 4 (July), 123:1--123:9. Google ScholarDigital Library
- Paris, S., Pettr, J., and Donikian, S. 2007. Pedestrian reactive navigation for crowd simulation: a predictive approach. Computer Graphics Forum 26, 3, 665--674.Google ScholarCross Ref
- Patil, S., van den Berg, J., Curtis, S., Lin, M. C., and Manocha, D. 2011. Directing crowd simulations using navigation fields. IEEE Transactions on Visualization and Computer Graphics 17 (February), 244--254. Google ScholarDigital Library
- Pellegrini, S., Ess, A., Schindler, K., and Van Gool, L. 2009. You'll never walk alone: Modeling social behavior for multi-target tracking. In Computer Vision, 2009 IEEE 12th International Conference on, 261--268.Google ScholarCross Ref
- Pettré, J., Ondřej, J., Olivier, A.-H., Cretual, A., and Donikian, S. 2009. Experiment-based modeling, simulation and validation of interactions between virtual walkers. In Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ACM, NY, USA, 189--198. Google ScholarDigital Library
- Reynolds, C. W. 1987. Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Computer Graphics 21, 25--34. Google ScholarDigital Library
- Reynolds, C. 1999. Steering behaviors for autonomous characters. In Game Developers Conference 1999, 763--782.Google Scholar
- Schadschneider, A. 2001. Cellular automaton approach to pedestrian dynamics - theory. 11.Google Scholar
- Treuille, A., Cooper, S., and Popović, Z. 2006. Continuum crowds. In SIGGRAPH '06, ACM, NY, USA, 1160--1168. Google ScholarDigital Library
- van den Berg, J., Lin, M., and Manocha, D. 2008. Reciprocal velocity obstacles for real-time multi-agent navigation. In IEEE International Conference on Robotics and Automation.Google Scholar
- van den Berg, J., Snape, J., Guy, S., and Manocha, D. 2011. Reciprocal collision avoidance with acceleration-velocity obstacles. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, 3475--3482.Google Scholar
- Van Den Berg, J., Guy, S. J., Lin, M., and Manocha, D. 2011. Reciprocal n-body collision avoidance. In Robotics Research. Springer, 3--19.Google Scholar
- Wolinski, D., Guy, S., Olivier, A.-H., Lin, M., Manocha, D., and Pettré, J. 2014. Parameter Estimation and Comparative Evaluation of Crowd Simulations. Computer Graphics Forum 33, 2, 303--312. Google ScholarDigital Library
- Zhou, B., Tang, X., and Wang, X. 2012. Coherent filtering: detecting coherent motions from crowd clutters. In Computer Vision-ECCV 2012. Springer, 857--871.Google Scholar
Index Terms
- WarpDriver: context-aware probabilistic motion prediction for crowd simulation
Recommendations
Crowd simulation by deep reinforcement learning
MIG '18: Proceedings of the 11th ACM SIGGRAPH Conference on Motion, Interaction and GamesSimulating believable virtual crowds has been an important research topic in many research fields such as industry films, computer games, urban engineering, and behavioral science. One of the key capabilities agents should have is navigation, which is ...
Position-based multi-agent dynamics for real-time crowd simulation
SCA '17: Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer AnimationExploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in ...
Geometric methods for multi-agent collision avoidance
SoCG '10: Proceedings of the twenty-sixth annual symposium on Computational geometryWe present an approach to reciprocal collision avoidance, where multiple mobile agents must avoid collisions with each other while moving in a common workspace. Each agent acts fully independently, and does not communicate with others. Yet our approach ...
Comments