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Velocity-based dynamic crowd simulation by data-driven optimization

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Abstract

A crowd simulator that generates realistic crowds with various movement patterns and environmental adaptability is urgently desired but underdeveloped for the applications of video games, urban visualization, autonomous driving, and robot navigation test. In this work, we present a novel velocity-based framework based on data-driven optimization to build dynamic crowd simulation that allows interactive control of global navigation, local collision avoidance, and group formation. An agent’s adaptive decision-making regarding its goals and dynamic local environment is formulated as an optimization problem which is solved by finding an optimal velocity from the real-world crowd velocity dataset. Each component that affects an agent’s movement is integrated into a velocity-based crowd energy metric to measure the similarity between the agent’s required simulated velocity and a given velocity sample. The proposed model can simulate thousands of agents at interactive rates. In addition, the framework is general and scalable to be integrated with various crowd simulation methods to meet the requirements of various kinds of robot navigation test. We validate our approach through simulation experiments in robot navigation scenarios, as well as comparisons to real-world crowd data and popular crowd simulation methods.

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Acknowledgements

Xiaogang Jin was supported by the National Natural Science Foundation of China (Grant No. 62036010), the Key Research and Development Program of Zhejiang Province (Grant No. 2020C03096), and the Ningbo Major Special Projects of the “Science and Technology Innovation 2025” (Grant No. 2020Z007).

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Author notes

  1. P. Liu, Q. Chao: Indicates equal contribution.

Authors and Affiliations

  1. State Key Lab of CAD &CG, Zhejiang University, Hangzhou, 310058, China

    Pengfei Liu, Zhongyuan Zhao, Qi Peng & Xiaogang Jin

  2. Harvard Medical School, Boston, USA

    Qianwen Chao & Henwei Huang

  3. Xidian University, Xian, China

    Qiongyan Wang

  4. MoreFun Studios, Tencent, Shenzhen, China

    Milo K. Yip & Elvis S. Liu

Authors
  1. Pengfei Liu

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  2. Qianwen Chao

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  3. Henwei Huang

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  4. Qiongyan Wang

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  5. Zhongyuan Zhao

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  6. Qi Peng

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  7. Milo K. Yip

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  8. Elvis S. Liu

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  9. Xiaogang Jin

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Correspondence toXiaogang Jin.

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Liu, P., Chao, Q., Huang, H.et al. Velocity-based dynamic crowd simulation by data-driven optimization.Vis Comput38, 3499–3512 (2022). https://doi.org/10.1007/s00371-022-02556-5

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