Crowd evacuation simulation based on hierarchical agent model and physics‐based character control
Crowd evacuation has gained increasing attention in recent years. The agent‐based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical mot...
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Published in | Computer animation and virtual worlds Vol. 35; no. 3 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Hoboken, USA
John Wiley & Sons, Inc
01.05.2024
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Abstract | Crowd evacuation has gained increasing attention in recent years. The agent‐based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical motion. In this article, we propose a hierarchical framework for crowd evacuation simulation, which combines the agent decision model with the agent motion model. In the decision model, we integrate emotional contagion and scene information to determine global path planning and local collision avoidance. In the motion model, we introduce a physics‐based character control method and control agent motion using deep reinforcement learning. Based on the decision strategy, the decision model can use a signal to control the agent motion in the motion model. Compared with existing methods, our framework can simulate physical interactions between agents and the environment. The results of the crowd evacuation simulation demonstrate that our framework can simulate crowd evacuation with physical fidelity. |
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AbstractList | Crowd evacuation has gained increasing attention in recent years. The agent‐based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical motion. In this article, we propose a hierarchical framework for crowd evacuation simulation, which combines the agent decision model with the agent motion model. In the decision model, we integrate emotional contagion and scene information to determine global path planning and local collision avoidance. In the motion model, we introduce a physics‐based character control method and control agent motion using deep reinforcement learning. Based on the decision strategy, the decision model can use a signal to control the agent motion in the motion model. Compared with existing methods, our framework can simulate physical interactions between agents and the environment. The results of the crowd evacuation simulation demonstrate that our framework can simulate crowd evacuation with physical fidelity. |
Author | Wu, Yanhui Liu, Zhen Liu, Tingting Ye, Jianming Wang, Yuanyi |
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References | 2015; 34 1995; 51 2012 2011 2010 2023; 16 2021; 29 2017; 23 2022; 23 2019; 38 2008 2019; 366 2020; 404 1996 1995 2020; 208 2016; 35 2010; 82 2018; 492 2023 2022 2013; 32 2010; 29 2020 2006; 25 2018 2017 2008; 21 2016 2015 2018; 51 2011; 22 (5):421–433 2021; 40 2021; 32(5):e1988 2018; 37 2016; 22 e_1_2_10_24_1 e_1_2_10_45_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_22_1 e_1_2_10_43_1 e_1_2_10_42_1 e_1_2_10_20_1 Liu L (e_1_2_10_36_1) 2010; 29 e_1_2_10_40_1 Kapadia M (e_1_2_10_5_1) 2015 e_1_2_10_2_1 Stuvel SA (e_1_2_10_23_1) 2017; 23 Liu Q (e_1_2_10_4_1) 2022 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_39_1 e_1_2_10_17_1 e_1_2_10_38_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_37_1 e_1_2_10_7_1 e_1_2_10_15_1 Olivier A‐H (e_1_2_10_9_1) 2011; 22 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_10_1 e_1_2_10_33_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 e_1_2_10_30_1 Zhuo L (e_1_2_10_25_1) 2021; 32 e_1_2_10_29_1 e_1_2_10_27_1 e_1_2_10_28_1 e_1_2_10_26_1 Xu M (e_1_2_10_41_1) 2018; 51 |
References_xml | – year: 2011 – volume: 32(5):e1988 year: 2021 article-title: Modeling crowd emotion from emergent event video publication-title: Comput Anim Virtual Worlds – volume: 16 start-page: 691 year: 2023 end-page: 714 article-title: Deep reinforcement learning and 3D physical environments applied to crowd evacuation in congested scenarios publication-title: Int J Digital Earth – volume: 34 start-page: 415 year: 2015 end-page: 423 article-title: Improving sampling‐based motion control publication-title: Comput Graph Forum – volume: 82 issue: 4 Pt 2 year: 2010 article-title: Generalized centrifugal‐force model for pedestrian dynamics publication-title: Phys Rev E Stat Nonlinear Soft Matter Phys – volume: 366 start-page: 314 year: 2019 end-page: 327 article-title: Data‐driven crowd evacuation: a reinforcement learning method publication-title: Neurocomputing – year: 1996 – volume: 22 (5):421–433 year: 2011 article-title: A step‐by‐step modeling, analysis and annotation of locomotion publication-title: Comput Anim Virtual Worlds – year: 2016 – volume: 38 start-page: 206:1 year: 2019 end-page: 206:11 article-title: DReCon: data‐driven responsive control of physics‐based characters publication-title: ACM Trans Graph – year: 2018 – volume: 32 start-page: 1 year: 2013 end-page: 11 article-title: Flexible muscle‐based locomotion for bipedal creatures publication-title: ACM Trans Graph – volume: 37 year: 2018 article-title: DeepMimic: example‐guided deep reinforcement learning of physics‐based character skills publication-title: ACM Trans Graph – year: 2010 – volume: 25 start-page: 1160 issue: 3 year: 2006 end-page: 1168 article-title: Continuum crowds publication-title: ACM Trans Graph – year: 2012 – volume: 29 start-page: 1 year: 2010 end-page: 9 article-title: Generalized biped walking control publication-title: ACM SIGGRAPH – volume: 29 start-page: 1 year: 2010 end-page: 10 article-title: Sampling‐based contact‐rich motion control publication-title: ACM SIGGRAPH Papers – volume: 35 start-page: 1 year: 2016 end-page: 14 article-title: Guided learning of control graphs for physics‐based characters publication-title: ACM Trans Graph – volume: 40 start-page: 1 year: 2021 end-page: 20 article-title: AMP: adversarial motion priors for stylized physics‐based character control publication-title: ACM Trans Graph – volume: 51 start-page: 4282 issue: 5 year: 1995 end-page: 4286 article-title: Social force model for pedestrian dynamics publication-title: Physical Review E, Statistical Physics, Plasmas, Fluids, Related Interdisciplinary Topics – volume: 404 start-page: 173 year: 2020 end-page: 185 article-title: Learning crowd behavior from real data: a residual network method for crowd simulation publication-title: Neurocomputing – volume: 492 start-page: 1107 year: 2018 end-page: 1119 article-title: Modified two‐layer social force model for emergency earthquake evacuation publication-title: Phys A: Stat Mech Appl – volume: 29 start-page: 2036 year: 2021 end-page: 2052 article-title: Heterogeneous crowd simulation using parametric reinforcement learning publication-title: IEEE Trans Visual Comput Graph – year: 2008 – year: 2022 – volume: 21 start-page: 85 year: 2008 end-page: 107 article-title: Pedestrian flows in bounded domains with obstacles publication-title: Cont Mech Thermodyn – volume: 208 year: 2020 article-title: Knowledge and emotion dual‐driven method for crowd evacuation publication-title: Knowl Based Syst – year: 2020 – year: 2023 – volume: 23 start-page: 1823 year: 2017 end-page: 1837 article-title: "torso crowds," IEEE transactions on visualization publication-title: Comput Graph – year: 1995 – volume: 51 start-page: 1567 year: 2018 end-page: 1581 article-title: Crowd behavior simulation with emotional contagion in unexpected multihazard situations publication-title: IEEE Trans Syst Man Cybern Syst – year: 2017 – volume: 23 start-page: 15476 year: 2022 end-page: 15486 article-title: Modeling crowd evacuation via behavioral heterogeneity‐based social force model publication-title: IEEE Trans Intell Transp Syst – year: 2022 article-title: Modeling the dynamics of pedestrian evacuation in a complex environment publication-title: Phys A: Stat Mech Appl – year: 2015 – volume: 22 start-page: 2145 year: 2016 end-page: 2159 article-title: Psychological parameters for crowd simulation: from audiences to mobs publication-title: IEEE Trans Visual Comput Graph – ident: e_1_2_10_2_1 doi: 10.1016/j.neucom.2019.08.021 – ident: e_1_2_10_44_1 – ident: e_1_2_10_14_1 doi: 10.1145/3588432.3591525 – ident: e_1_2_10_38_1 doi: 10.1145/3355089.3356536 – ident: e_1_2_10_10_1 doi: 10.1103/PhysRevE.82.046111 – ident: e_1_2_10_24_1 doi: 10.1109/TVCG.2015.2501801 – volume-title: Virtual crowds: steps toward behavioral realism year: 2015 ident: e_1_2_10_5_1 – ident: e_1_2_10_6_1 doi: 10.1145/3424636.3426894 – ident: e_1_2_10_39_1 – ident: e_1_2_10_18_1 doi: 10.1103/PhysRevE.51.4282 – ident: e_1_2_10_16_1 doi: 10.1007/s00161-009-0100-x – volume: 29 start-page: 1 year: 2010 ident: e_1_2_10_36_1 article-title: Sampling‐based contact‐rich motion control publication-title: ACM SIGGRAPH Papers doi: 10.1145/1778765.1778865 – ident: e_1_2_10_15_1 doi: 10.1145/1141911.1142008 – ident: e_1_2_10_3_1 doi: 10.1109/TITS.2022.3140823 – ident: e_1_2_10_21_1 doi: 10.1145/3574131.3574445 – ident: e_1_2_10_28_1 doi: 10.1080/17538947.2023.2182376 – ident: e_1_2_10_40_1 – ident: e_1_2_10_12_1 doi: 10.1111/cgf.12806 – ident: e_1_2_10_43_1 – ident: e_1_2_10_11_1 doi: 10.1111/cgf.12571 – ident: e_1_2_10_22_1 doi: 10.1145/3190834.3190839 – ident: e_1_2_10_35_1 doi: 10.1145/2786784.2786802 – volume: 23 start-page: 1823 year: 2017 ident: e_1_2_10_23_1 article-title: "torso crowds," IEEE transactions on visualization publication-title: Comput Graph – ident: e_1_2_10_37_1 doi: 10.1145/3197517.3201311 – ident: e_1_2_10_13_1 doi: 10.1145/3450626.3459670 – ident: e_1_2_10_7_1 doi: 10.1145/3528233.3530712 – ident: e_1_2_10_26_1 doi: 10.1016/j.knosys.2020.106451 – ident: e_1_2_10_30_1 doi: 10.1145/1778765.1781156 – ident: e_1_2_10_27_1 doi: 10.1109/TVCG.2021.3139031 – ident: e_1_2_10_32_1 doi: 10.1145/2508363.2508399 – ident: e_1_2_10_20_1 doi: 10.1109/ITSC55140.2022.9922479 – volume: 22 year: 2011 ident: e_1_2_10_9_1 article-title: A step‐by‐step modeling, analysis and annotation of locomotion publication-title: Comput Anim Virtual Worlds – ident: e_1_2_10_42_1 – ident: e_1_2_10_34_1 doi: 10.1109/IROS.2012.6386025 – ident: e_1_2_10_17_1 – ident: e_1_2_10_19_1 doi: 10.1016/j.physa.2017.11.041 – ident: e_1_2_10_31_1 doi: 10.1109/ICRA.2016.7487294 – ident: e_1_2_10_33_1 doi: 10.1145/1833349.1778811 – volume: 51 start-page: 1567 year: 2018 ident: e_1_2_10_41_1 article-title: Crowd behavior simulation with emotional contagion in unexpected multihazard situations publication-title: IEEE Trans Syst Man Cybern Syst – ident: e_1_2_10_45_1 – volume: 32 year: 2021 ident: e_1_2_10_25_1 article-title: Modeling crowd emotion from emergent event video publication-title: Comput Anim Virtual Worlds doi: 10.1002/cav.1988 – ident: e_1_2_10_8_1 doi: 10.1016/j.neucom.2020.04.141 – year: 2022 ident: e_1_2_10_4_1 article-title: Modeling the dynamics of pedestrian evacuation in a complex environment publication-title: Phys A: Stat Mech Appl – ident: e_1_2_10_29_1 doi: 10.1145/218380.218414 |
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SubjectTerms | agent model Collision avoidance Control methods crowd simulation deep reinforcement learning Evacuation physics‐based character control Simulation |
Title | Crowd evacuation simulation based on hierarchical agent model and physics‐based character control |
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