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Virtual cityscapes: recent advances in crowd modeling and traffic simulation

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Abstract

We survey our recent work on interactive modeling, generation, and control of large-scale crowds and traffic for simulating digital cities. These include multi-agent navigation, simulating large crowds with emerging behaviors as well as interactive simulation of traffic on large road networks. We also highlight their performance on different scenarios.

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References

  1. Guy S, Chhugani J, Kim C, Satish N, Lin M C, Manocha D, Dubey P. Clearpath: Highly parallel collision avoidance for multi-agent simulation. In: Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2009, 177–187

  2. Narain R, Golas A, Curtis S, Lin M C. Aggregate dynamics for dense crowd simulation. In: ACM Transactions on Graphics (Proc. of ACM SIGGRAPH Asia), 2009

  3. Patil S, Berg J V D, Curtis S, Lin M C, Manocha D. Directing crowd simulations using navigation fields. Technical report. Department of Computer Science, University of North Carolina, May 2009

  4. Pelechano N, Allbeck J M, Badler N I. Virtual Crowds: Methods, Simulation and Control. Morgan and Claypool Publishers, 2008

  5. Helbing D, Molnar P. Social force model for pedestrian dynamics. Physical Review E, 1995, 51: 4282

    Article  Google Scholar 

  6. Reynolds CW. Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 1987, 21(4): 25–34

    Article  MathSciNet  Google Scholar 

  7. Reynolds C W. Steering behaviors for autonomous characters. In: Game Developers Conference, 1999

  8. Berg J V D, Guy S J, Lin M, Manocha D. Reciprocal n-body collision avoidance. International Symposium of Robotics Research, 2009

  9. van den Berg J, Seawall J, Lin M C, Manocha D. Virtualized tra_c: Reconstructing traffic flows from discrete spatio-temporal data. In: Proceedings of IEEE Virtual Reality Conference, 2009, 183–190

  10. Yersin B, Maim J, Ciechomski P, Schertenleib S, Thalmann D. Steering a virtual crowd based on a semantically augmented navigation graph. In: VCROWDS 2005, 2005

  11. Pettré J, -Ondøej J, Olivier A, Cretual A, Donikian S. Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: Symposium on Computer Animation, ACM, 2009, 189–198

  12. Chenney S. Flow tiles. In: Proceedings of 2004 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 2004

  13. Jin X, Xu J, Wang C C L, Huang S, Zhang J. Interactive control of large crowd navigation in virtual environment using vector field. In IEEE Computer Graphics and Application, 2008

  14. Treuille A, Cooper S, Popovic Z. Continuum crowds. In: Proceedings of ACM SIGGRAPH, 2006, 1160–1168

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

  16. Still G. Crowd Dynamics. PhD thesis, University of Warwick, UK, 2000

    Google Scholar 

  17. Karamouzas I, Heil P, Beek P, Overmars M. A predictive collision avoidance model for pedestrian simulation. In: Proceedings of Motion in Games, 2009, 41–52

  18. Sarmady S, Haron F, Hj A Z. Modeling groups of pedestrians in least effort crowd movements using cellular automata. In: Proceedings of 3rd Asia International Conference on Modeling and Simulation, 2009, 520–525

  19. Kagarlis M. Method and apparatus of simulating movement of an autonomous entity through an environment. United States Patent No. US 7,188,056, Sep. 2002

  20. Guy S, Chuggani J, Curtis S, Dubey P, Lin M, Manocha D. Pledestrians: A least-effort approach to crowd simulation. Technical report. Department of Computer Science, University of North Carolina, 2010

  21. Shao W, Terzopoulos D. Autonomous pedestrians. In: SCA’05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation. New York: ACM Press, 2005, 19–28

    Chapter  Google Scholar 

  22. Yu Q, Terzopoulos D. A decision network framework for the behavioral animation of virtual humans. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2007, 119–128

  23. Pelechano N, Allbeck J M, Badler N I. Controlling individual agents in highdensity crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2007, 99–108

  24. Antonini G, Martinez S V, Bierlaire M, Thiran J P. Behavioral priors for detection and tracking of pedestrians in video sequences. International Journal of Computer Vision, 2006, 69(2): 159–180

    Article  Google Scholar 

  25. Johansson A, Helbing D, Shukla P K. Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Advances in Complex Systems, 2007, 1 (Suppl 2): 271–288

    Article  MathSciNet  Google Scholar 

  26. Scovanner P, Tappen M F. Learning pedestrian dynamics from the real world. In: Proceedings of 2009 International Conference on Computer Vision (ICCV), 2009

  27. Kang Y, Park S, Lee E. An efficient control over human running animation with extension of planar hopper model. In: Pacific Graphics, 1998, 169–176

  28. Juang J. Minimal energy control on trajectory generation. In: International Conference on Information Intelligence and Systems, 1999, 204

  29. Channon P H, Hopkins S H, Phan D T. Derivation of optimal walking motions for a biped walking robot. Robotica, 1992, 10: 165–172

    Article  Google Scholar 

  30. Roussel L, Canudas de Wit C, Goswami A. Generation of energy optimal complete gait cycles for biped robots. IEEE Transactions on Robotics and Automation, 1998, 16: 2036–2041

    Google Scholar 

  31. Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles. International Journal of Robotics Research, 1998, 17(7): 760–772

    Article  Google Scholar 

  32. van den Berg J, Patil S, Seawall J, Manocha D, Lin MC. Interactive navigation of individual agents in crowded environments. In: Proceedings of ACM Symposium on Interactive 3D Graphics and Games, 2008, 139–147

  33. van den Berg J, Lin M C, Manocha D. Reciprocal velocity obstacles for realtime multi-agent navigation. In: Proceedings of IEEE Conference on Robotics and Automation, 2008, 1928–1935

  34. Chowdhury D, Santen L, Schadschneider A. Statistical physics of vehicular traffic and some related systems. Physics Reports, 2000, 329(4–6): 199–329

    Article  MathSciNet  Google Scholar 

  35. Aw A, Rascle M. Resurrection of “second order” models of traffic flow. SIAM Journal of Applied Mathmatics, 2000, (60): 916–938

  36. Zhang H M. A non-equilibrium traffic model deviod of gas-like behavior. Transportation Research Part B: Methodological, 2002, 36(3): 275–290

    Article  Google Scholar 

  37. Sewall J, Welkie D, Merrell P, Lin M C. Continuum traffic simulation. In: Proceedings of Eurographics, 2010

  38. Clark C M, Bretl T, Rock S. Applying kinodynamic randomized motion planning with a dynamic priority system to multi-robot space systems. In: Proceedings of IEEE Aerospace Conference, 2002, 7: 3621–3631

    Google Scholar 

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Correspondence to Dinesh Manocha.

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Lin, M.C., Manocha, D. Virtual cityscapes: recent advances in crowd modeling and traffic simulation. Front. Comput. Sci. China 4, 405–416 (2010). https://doi.org/10.1007/s11704-010-0119-y

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Keywords

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