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.

This is a preview of subscription content, access via your institution.


  1. 1

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

    Article  Google Scholar 

  2. 2

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  11. 11

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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  14. 14

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

    Book  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  17. 17

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

    Article  Google Scholar 

  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

    Google Scholar 

  19. 19

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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  24. 24

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

    Google Scholar 

  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

    Google Scholar 

  26. 26

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

    Article  Google Scholar 

  27. 27

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

    Google Scholar 

  28. 28

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

    Article  Google Scholar 

  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

    Google Scholar 

  30. 30

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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Angelos Kremyzas.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


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