CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds

  • Sébastien Paris
  • Anton Gerdelan
  • Carol O’Sullivan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5884)


The new wave of computer-driven entertainment technology throws audiences and game players into massive virtual worlds where entire cities are rendered in real time. Computer animated characters run through inner-city streets teeming with pedestrians, all fully rendered with 3D graphics, animations, particle effects and linked to 3D sound effects to produce more realistic and immersive computer-hosted entertainment experiences than ever before. Computing all of this detail at once is enormously computationally expensive, and game designers as a rule, have sacrificed the behavioural realism in favour of better graphics. In this paper we propose a new Collision Avoidance Level of Detail (CA-LOD) algorithm that allows games to support huge crowds in real time with the appearance of more intelligent behaviour. We propose two collision avoidance models used for two different CA-LODs: a fuzzy steering focusing on the performances, and a geometric steering to obtain the best realism. Mixing these approaches allows to obtain thousands of autonomous characters in real time, resulting in a scalable but still controllable crowd.


Path Planning Collision Avoidance Obstacle Avoidance Static Obstacle Virtual Human 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Hamill, J., O’Sullivan, C.: Virtual dublin - a framework for real-time urban simulation. In: Proc. of the Winter Conference on Computer Graphics, vol. 11, pp. 1–3 (2003)Google Scholar
  2. 2.
    Peters, C., Ennis, C.: Modeling groups of plausible virtual pedestrians. IEEE Computer Graphics and Applications 29(4), 54–63 (2009)CrossRefGoogle Scholar
  3. 3.
    Wimmer, M., Bittner, J.: Hardware occlusion queries made useful. In: Pharr, M., Fernando, R. (eds.) GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation. Addison-Wesley, Reading (2005)Google Scholar
  4. 4.
    Luebke, D., Watson, B., Cohen, J.D., Reddy, M., Varshney, A.: Level of Detail for 3D Graphics. Elsevier Science Inc., New York (2002)Google Scholar
  5. 5.
    Dobbyn, S., Hamill, J., O’Conor, K., O’Sullivan, C.: Geopostors: a real-time geometry/impostor crowd rendering system. ACM Trans. Graph. 24(3), 933 (2005)CrossRefGoogle Scholar
  6. 6.
    Yersin, B., Maim, J., Pettré, J., Thalmann, D.: Crowd Patches: Populating Large-Scale Virtual Environments for Real-Time Applications. In: I3D 2009 (2009)Google Scholar
  7. 7.
    Pettré, J., de Ciechomski, P.H., Maïm, J., Yersin, B., Laumond, J.P., Thalmann, D.: Real-time navigating crowds: scalable simulation and rendering. Computer Animation and Virtual Worlds 17(3-4), 445–455 (2006)CrossRefGoogle Scholar
  8. 8.
    Paris, S., Donikian, S.: Activity-driven populace: a cognitive approach for crowd simulation. Computer Graphics and Applications (CGA) special issue Virtual Populace 29(4), 24–33 (2009)Google Scholar
  9. 9.
    Yu, Q., Terzopoulos, D.: A decision network framework for the behavioral animation of virtual humans. In: Metaxas, D., Popovic, J. (eds.) Eurographics/ ACM SIGGRAPH Symposium on Computer Animation, pp. 119–128 (2007)Google Scholar
  10. 10.
    O’Sullivan, C., Cassell, J., Vilhjálmsson, H., Dingliana, J., Dobbyn, S., McNamee, B., Peters, C., Giang, T.: Levels of detail for crowds and groups. Computer Graphics Forum 21(4), 733–741 (2003)CrossRefGoogle Scholar
  11. 11.
    Paris, S., Donikian, S., Bonvalet, N.: Environmental abstraction and path planning techniques for realistic crowd simulation. Computer Animation and Virtual Worlds 17, 325–335 (2006)CrossRefGoogle Scholar
  12. 12.
    Reynolds, C.W.: Steering behaviors for autonomous characters. In: Game Developers Conference 1999 (1999)Google Scholar
  13. 13.
    Lamarche, F., Donikian, S.: Crowds of virtual humans: a new approach for real time navigation in complex and structured environments. Computer Graphics Forum 23, 509–518 (2004)CrossRefGoogle Scholar
  14. 14.
    Paris, S., Pettré, J., Donikian, S.: Pedestrian reactive navigation for crowd simulation: a predictive approach. In: Computer Graphics Forum, Eurographics 2007, vol. 26(3), pp. 665–674 (2007)Google Scholar
  15. 15.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science 39(1), 1–24 (2005)CrossRefGoogle Scholar
  16. 16.
    Paris, S., Mekni, M., Moulin, B.: Informed virtual geographic environments: an accurate topological approach. In: The International Conference on Advanced Geographic Information Systems & Web Services (GEOWS). IEEE Computer Society Press, Los Alamitos (2009)Google Scholar
  17. 17.
    Choset, H.M., Hutchinson, S., Lynch, K.M., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  18. 18.
    Gerdelan, A.P.: A solution for streamlining intelligent agent-based traffic into 3d simulations and games. Technical Report CSTN-072, IIMS, Massey University, North Shore 102-904, Auckland, New Zealand (January 2009)Google Scholar
  19. 19.
    Gerdelan, A.P.: Driving intelligence: A new architecture and novel hybrid algorithm for next-generation urban traffic simulation. Technical Report CSTN-079, Institute of Information and Mathematical Sciences, Massey University, North Shore 102-904, Auckland, New Zealand (February 2009)Google Scholar
  20. 20.
    Dougherty, M., Fox, K., Cullip, M., Boero, M.: Technological advances that impact on microsimulation modelling. Transport Reviews 20(2), 145–171 (2000)CrossRefGoogle Scholar
  21. 21.
    Gerdelan, A.P., Reyes, N.H.: Towards a generalised hybrid path-planning and motion control system with auto-calibration for animated characters in 3d environments. In: Advances in Neuro-Information Processing. LNCS, vol. 5507, pp. 25–28. Springer, Heidelberg (2008)Google Scholar
  22. 22.
    Gerdelan, A.P.: Architecture design for self-training intelligent vehicle-driving agents: paradigms and tools. Technical Report CSTN-088, Institute of Information and Mathematical Sciences, Massey University, North Shore 102-904, Auckland, New Zealand (April 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sébastien Paris
    • 1
  • Anton Gerdelan
    • 1
  • Carol O’Sullivan
    • 1
  1. 1.GV2Trinity College DublinIreland

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