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Fast Simulation of Crowd Collision Avoidance

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Advances in Computer Graphics (CGI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11542))

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Abstract

Real-time large-scale crowd simulations with realistic behavior, are important for many application areas. On CPUs, the ORCA pedestrian steering model is often used for agent-based pedestrian simulations. This paper introduces a technique for running the ORCA pedestrian steering model on the GPU. Performance improvements of up to 30 times greater than a multi-core CPU model are demonstrated. This improvement is achieved through a specialized linear program solver on the GPU and spatial partitioning of information sharing. This allows over 100,000 people to be simulated in real time (60 frames per second).

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References

  1. Abe, Y., Yoshiki, M.: Collision avoidance method for multiple autonomous mobile agents by implicit cooperation. In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), vol. 3, pp. 1207–1212, October 2001. https://doi.org/10.1109/IROS.2001.977147

  2. Barut, O., Haciomeroglu, M., Sezer, E.A.: Combining GPU-generated linear trajectory segments to create collision-free paths for real-time ambient crowds. Graph. Models 99, 31–45 (2018). https://doi.org/10.1016/j.gmod.2018.07.002

    Article  MathSciNet  Google Scholar 

  3. van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 1928–1935, May 2008. https://doi.org/10.1109/ROBOT.2008.4543489

  4. van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. Springer Tracts in Advanced Robotics, vol. 70, pp. 3–19. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19457-3_1

    Chapter  Google Scholar 

  5. Bleiweiss, A.: Multi agent navigation on the GPU. White paper, GDC, vol. 9 (2009)

    Google Scholar 

  6. Blue, V., Adler, J.: Emergent fundamental pedestrian flows from cellular automata microsimulation—request PDF. Transp. Res. Rec. J. Transp. Res. Board 1644, 29–36 (1998). https://doi.org/10.3141/1644-04

    Article  Google Scholar 

  7. Blue, V., Adler, J.: Cellular automata microsimulation of bidirectional pedestrian flows. Transp. Res. Rec. J. Transp. Res. Board 1678, 135–141 (1999). https://doi.org/10.3141/1678-17

    Article  Google Scholar 

  8. Charlton, J., Maddock, S., Richmond, P.: Two-dimensional batch linear programming on the GPU. J. Parallel Distrib. Comput. 126, 152–160 (2019). https://doi.org/10.1016/j.jpdc.2019.01.001

    Article  Google Scholar 

  9. Fickett, M., Zarko, L.: GPU Continuum Crowds. CIS Final Project Final report, University of Pennsylvania (2007)

    Google Scholar 

  10. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Rob. Res. 17(7), 760–772 (1998). https://doi.org/10.1177/027836499801700706

    Article  Google Scholar 

  11. Fulgenzi, C., Spalanzani, A., Laugier, C.: Dynamic obstacle avoidance in uncertain environment combining PVOs and occupancy grid. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 1610–1616. IEEE, Rome, April 2007. https://doi.org/10.1109/ROBOT.2007.363554

  12. Guy, S.J., et al.: ClearPath: highly parallel collision avoidance for multi-agent simulation. In: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2009, pp. 177–187. ACM, New York (2009). https://doi.org/10.1145/1599470.1599494

  13. He, L., Pan, J., Narang, S., Wang, W., Manocha, D.: Dynamic Group Behaviors for Interactive Crowd Simulation. arXiv:1602.03623 [cs], February 2016

  14. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995). https://doi.org/10.1103/PhysRevE.51.4282

    Article  Google Scholar 

  15. Karmakharm, T., Richmond, P.: Agent-based large scale simulation of pedestrians with adaptive realistic navigation vector fields. EG UK Theor. Pract. Comput. Graph. 10, 67–74 (2010)

    Google Scholar 

  16. Kluge, B., Prassler, E.: Recursive probabilistic velocity obstacles for reflective navigation. In: Yuta, S., Asama, H., Prassler, E., Tsubouchi, T., Thrun, S. (eds.) Field and Service Robotics: Recent Advances in Research and Applications. Springer Tracts in Advanced Robotics, vol. 24, pp. 71–79. Springer, Berlin (2006). https://doi.org/10.1007/10991459_8

    Chapter  Google Scholar 

  17. Li, B., Mukundan, R.: A Comparative Analysis of Spatial Partitioning Methods for Large-Scale, Real-Time Crowd Simulation. Václav Skala - UNION Agency (2013)

    Google Scholar 

  18. Narain, R., Golas, A., Curtis, S., Lin, M.C.: Aggregate dynamics for dense crowd simulation. In: ACM SIGGRAPH Asia 2009 Papers, SIGGRAPH Asia 2009, pp. 122:1–122:8. ACM, New York (2009). https://doi.org/10.1145/1661412.1618468

  19. Nvidia: Tuning CUDA Applications for Maxwell (2018). http://docs.nvidia.com/cuda/maxwell-tuning-guide/index.html

  20. Pettré, J., Kallmann, M., Lin, M.C.: Motion planning and autonomy for virtual humans. In: ACM SIGGRAPH 2008 Classes, SIGGRAPH 2008, pp. 42:1–42:31. ACM, New York (2008). https://doi.org/10.1145/1401132.1401193

  21. Pettré, J., Pelechano, N.: Introduction to crowd simulation. In: Bousseau, A., Gutierrez, D. (eds.) EG 2017 - Tutorials. The Eurographics Association (2017). https://doi.org/10.2312/egt.20171029

  22. Richmond, P.: Flame GPU Technical Report and User Guide. Department of Computer Science Technical report CS-11-03, University of Sheffield (2011)

    Google Scholar 

  23. Richmond, P., Romano, D.M.: A high performance framework for agent based pedestrian dynamics on GPU hardware. In: Proceedings of EUROSIS ESM 2008 (2008)

    Google Scholar 

  24. Schönfisch, B., de Roos, A.: Synchronous and asynchronous updating in cellular automata. Biosystems 51(3), 123–143 (1999). https://doi.org/10.1016/S0303-2647(99)00025-8

    Article  Google Scholar 

  25. Seidel, R.: Small-dimensional linear programming and convex hulls made easy. Discrete Comput. Geom. 6(3), 423–434 (1991). https://doi.org/10.1007/BF02574699

    Article  MathSciNet  MATH  Google Scholar 

  26. Snape, J.: Optimal Reciprocal Collision Avoidance (C++). Contribute to snape/RVO2 development by creating an account on GitHub, March 2019

    Google Scholar 

  27. Thalmann, D.: Populating virtual environments with crowds. In: Proceedings of the 2006 ACM International Conference on Virtual Reality Continuum and Its Applications, VRCIA 2006, p. 11. ACM, New York (2006). https://doi.org/10.1145/1128923.1128925

  28. Wang, Y., Davidson, A., Pan, Y., Wu, Y., Riffel, A., Owens, J.D.: Gunrock: a high-performance graph processing library on the GPU. In: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, p. 11. ACM (2016)

    Google Scholar 

  29. Xu, M.L., Jiang, H., Jin, X., Deng, Z.: Crowd simulation and its applications: recent advances. J. Comput. Sci. Technol. 29, 799–811 (2014). https://doi.org/10.1007/s11390-014-1469-y

    Article  Google Scholar 

  30. Yang, Z., Pan, J., Wang, W., Manocha, D.: Proxemic group behaviors using reciprocal multi-agent navigation. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 292–297 (2016). https://doi.org/10.1109/ICRA.2016.7487147

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Acknowledgements

This research was supported by the Transport Systems Catapult and the National Council of Science and Technology in Mexico (Consejo Nacional de Ciencia y Tecnología, CONACYT).

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Authors and Affiliations

  1. University of Sheffield, Sheffield, S10 2TN, UK

    John Charlton, Luis Rene Montana Gonzalez, Steve Maddock & Paul Richmond

Authors
  1. John Charlton
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  2. Luis Rene Montana Gonzalez
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  3. Steve Maddock
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  4. Paul Richmond
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Corresponding author

Correspondence to John Charlton .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

  2. Bournemouth University, Poole, UK

    Jian Chang

  3. University of Geneva, MIRALab, Carouge, Switzerland

    Nadia Magnenat Thalmann

  4. International Christian University, Tokyo, Japan

    Eckhard Hitzer

  5. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

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Charlton, J., Gonzalez, L.R.M., Maddock, S., Richmond, P. (2019). Fast Simulation of Crowd Collision Avoidance. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_22

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  • DOI: https://doi.org/10.1007/978-3-030-22514-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22513-1

  • Online ISBN: 978-3-030-22514-8

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