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Towards social behavior in virtual-agent navigation


We present Social Groups and Navigation (SGN), a method to simulate the walking behavior of small pedestrian groups in virtual environments. SGN is the first method to simulate group behavior on both global and local levels of an underlying planning hierarchy. We define quantitative metrics to measure the coherence and the sociality of a group based on existing empirical data of real crowds. SGN does not explicitly model coherent and social formations, but it lets such formations emerge from simple geometric rules. In addition to a previous version, SGN also handles group-splitting to smaller groups throughout navigation as well as social sub-group behavior whenever a group has to temporarily split up to re-establish its coherence. For groups of four, SGN generates between 13% and 53% more socially-friendly behavior than previous methods, measured over the lifetime of a group in the simulation. For groups of three, the gain is between 15% and 31%, and for groups of two, the gain is between 1% and 4%. SGN is designed in a flexible way, and it can be integrated into any crowd-simulation framework that handles global path planning and any path following as separate steps. Experiments show that SGN enables the simulation of thousands of agents in real time.

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  1. 1

    James J. The distribution of free-forming small group size. Amer Sociol Rev, 1953, 18: 569–570

  2. 2

    Coleman J S, James J. The equilibrium size distribution of freely-forming groups. Sociometry, 1961, 24: 36–45

  3. 3

    Moussaíd M, Perozo N, Garnier S, et al. The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS ONE, 2010, 5: e10047

  4. 4

    Kimmel A, Dobson A, Bekris K. Maintaining team coherence under the velocity obstacle framework. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), Richland, 2012. 1: 247–256

  5. 5

    Karamouzas I, Overmars M. Simulating and evaluating the local behavior of small pedestrian groups. IEEE Trans Vis Comput Graph, 2012, 18: 394–406

  6. 6

    Wu Q Q, Ji Q G, Du J H, et al. Simulating the local behavior of small pedestrian groups using synthetic-vision based steering approach. In: Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry (VRCAI 2013). New York: ACM, 2013. 41–50

  7. 7

    Jaklin N, Kremyzas A, Geraerts R. Adding sociality to virtual pedestrian groups. In: Proceedings of the 21st ACM Symposium on Virtual Reality Software and Technology (VRST 2015). New York: ACM, 2015. 163–172

  8. 8

    Moussaíd M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proc Nat Acad Sci USA, 2011, 108: 6884–6888

  9. 9

    Geraerts R. Planning short paths with clearance using explicit corridors. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, 2010. 1997–2004

  10. 10

    Kallmann M. Shortest paths with arbitrary clearance from navigation meshes. In: Proceedings of the 9th Eurographics/SIGGRAPH Symposium on Computer Animation (SCA 2010). Switzerland: Eurographics Association Aire-la-Ville, 2010. 159–168

  11. 11

    Oliva R, Pelechano N. Clearance for diversity of agents’ sizes in navigation meshes. Comput Graph, 2015, 47: 48–58

  12. 12

    Curtis S, Best A, Manocha D. Menge: a modular framework for simulating crowd movement. Technical Report. University of North Carolina at Chapel Hill, 2014

  13. 13

    van Toll W, Jaklin N, Geraerts R. Towards believable crowds: a generic multi-level framework for agent navigation. In: Proceedings of the 20th Annual Conference of the Advanced School for Computing and Imaging, Amersfoort, 2015

  14. 14

    Thalmann D, Musse S R. Crowd Simulation. 2nd ed. Berlin: Springer, 2013

  15. 15

    Pelechano N, Allbeck J, Badler N. Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation). San Rafael: Morgan and Claypool Publishers, 2008

  16. 16

    Musse S, Thalmann D. A model of human crowd behavior: group inter-relationship and collision detection analysis. In: Thalmann D, van der Panne M, eds. Proceedings of the Eurographics Workshop in Budapest, Hungary, 1997. 39–51

  17. 17

    Qiu F S, Hu X L. Modeling group structures in pedestrian crowd simulation. Simul Model Pract Theory, 2010, 18: 190–205

  18. 18

    Kamphuis A, Overmars M. Finding paths for coherent groups using clearance. In: Proceedings of the 3rd ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA 2004). Switzerland: Eurographics Association Aire-la-Ville, 2004. 19–28

  19. 19

    Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles. Int J Robot Res, 1998, 17: 760–772

  20. 20

    van den Berg J, Guy S, Lin M, et al. Reciprocal n-body collision avoidance. In: Pradalier C, Siegwart R, Hirzinger G, eds. Robotics Research. Berlin/Heidelberg: Springer, 2011. 3–19

  21. 21

    Park S I, Quek F, Cao Y. Modeling social groups in crowds using common ground theory. In: Proceedings of the Winter Simulation Conference (WSC 2012), Berlin, 2012. 113

  22. 22

    Huang T Y, Kapadia M, Badler N I, et al. Path planning for coherent and persistent groups. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong, 2014. 1652–1659

  23. 23

    Ondřej J, Pettré J, Olivier A-H, et al. A synthetic-vision based steering approach for crowd simulation. ACM Trans Graph, 2010, 29: 123

  24. 24

    Fruin J J. Pedestrian Planning and Design. New York: Metropolitan Association of Urban Designers and Environmental Planners, 1971

  25. 25

    Karamouzas I, Geraerts R, Overmars M. Indicative routes for path planning and crowd simulation. In: Proceedings of the 4th International Conference on Foundations of Digital Games. New York: ACM, 2009. 113–120

  26. 26

    Jaklin N, Cook IV A, Geraerts R. Real-time path planning in heterogeneous environments. Comput Animat Virtual Worlds, 2013, 24: 285–295

  27. 27

    Zipf G K. Human Behavior and the Principle of Least Effort. Boston: Addison-Wesley Press, 1949

  28. 28

    Costa M. Interpersonal distances in group walking. J Nonverbal Behav, 2010, 34: 15–26

  29. 29

    Fridman N, Kaminka G A, Zilka A. The impact of culture on crowd dynamics: an empirical approach. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2013), Richland, 2013. 143–150

  30. 30

    Weidmann U. Transporttechnik der fussgänger. IVT, Institut für Verkehrsplanung, Transporttechnik, Strassen-und Eisenbahnbau, 90, 1992

  31. 31

    Köster G, Treml F, Seitz M, et al. Validation of crowd models including social groups. In: Weidmann U, Kirsch U, Schreckenberg M, eds. Pedestrian and Evacuation Dynamics 2012. Switzerland: Springer International Publishing, 2014. 1051–1063

  32. 32

    Liddle J, Seyfried A, Steffen B, et al. Microscopic insights into pedestrian motion through a bottleneck, resolving spatial and temporal variations. arXiv:1105.1532v1

  33. 33

    Xu S, Duh H B-L. A simulation of bonding effects and their impacts on pedestrian dynamics. IEEE Trans Intell Transp Syst, 2010, 11: 153–161

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Correspondence to Angelos Kremyzas.

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Kremyzas, A., Jaklin, N. & Geraerts, R. Towards social behavior in virtual-agent navigation. Sci. China Inf. Sci. 59, 112102 (2016).

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  • virtual humans
  • social-group behavior
  • autonomous agent navigation
  • crowd simulation