Emotion Contagion Model for Dynamical Crowd Path Planning

Yunpeng Wu; Xiangming Huang; Zhen Tian; Xiaoqiang Yan; Hui Yu

doi:10.53941/ijndi.2024.100014

Abstract

Crowd path planning aims to find the optimal path between the source and destination for multiple agents in crowd scenes. The advent of parallel theory and digital twin technologies provides a novel platform for simulating crowd path planning, which has become increasingly popular in various applications, such as pedestrian evacuation, intelligent transportation, and civil planning. The widely used strategy for crowd path planning emphasizes the objective factors, such as user-specific guidance, shortest path and crowd density. However, this strategy ignores the subjective emotion of agents, which can have significant impact on the diverse path choices of each agent. To tackle this challenge, we present a novel Emotion Contagion Model (ECM) to dynamically conduct path planning in crowded environments by incorporating the emotion of each agent. The proposed method provides a solution to the long-standing high-level affective issue for virtual agents during path search. Firstly, to bridge the gap between emotion states and path choices, the emotion preference is defined based on personality traits of multiple agents. Secondly, an emotion contagion algorithm is proposed to recognize the collective patterns of these agents, which can reveal the dynamical variation of emotion preference under crowded complex environments. Finally, to solve the emotion-to-path mapping, we propose a leastexpected-time objective function to find the optimal path choice for each agent according to the navigation graph in the given scenario. Experimental results on various scenarios, including the subway station, railway station square, fire evacuation and indoor environment, verify the effectiveness of the ECM compared with the state-of-the-art methods.

References

1.
Li, Y.; Lu, X.Y.; Wang, J.Q.; et al. Pedestrian trajectory prediction combining probabilistic reasoning and sequence learning. IEEE Trans. Intell. Veh., 2020, 5: 461−474. doi: 10.1109/TIV.2020.2966117
2.
Liao, X.S.; Zhao, X.P.; Wang, Z.R.; et al. Game theory-based ramp merging for mixed traffic with unity-sumo co-simulation. IEEE Trans. Syst., Man, Cybern.: Syst., 2022, 52: 5746−5757. doi: 10.1109/TSMC.2021.3131431
3.
Wu, Y.; Low, K.H.; Pang, B.Z.; et al. Swarm-based 4D path planning for drone operations in urban environments. IEEE Trans. Veh. Technol., 2021, 70: 7464−7479. doi: 10.1109/TVT.2021.3093318
4.
Yan, X.Q.; Mao, Y.Q.; Li, M.Y.; et al. Multitask image clustering via deep information bottleneck. IEEE Trans. Cybern., 2024, 54: 1868−1881. doi: 10.1109/TCYB.2023.3273535
5.
Yan, X.Q.; Mao, Y.Q.; Ye, Y.D.; et al. Cross-modal clustering with deep correlated information bottleneck method. IEEE Trans. Neural Netw. Learn. Syst. 2023 , in press. doi: 10.1109/TNNLS.2023.3269789.
6.
Yan, X.Q.; Mao, Y.Q.; Ye, Y.D.; et al. Explanation guided cross-modal social image clustering. Inf. Sci., 2022, 593: 1−16. doi: 10.1016/j.ins.2022.01.065
7.
Yan, X.Q.; Shi, K.Y.; Ye, Y.D.; et al. Deep correlation mining for multi-task image clustering. Expert Syst. Appl., 2022, 187: 115973. doi: 10.1016/j.eswa.2021.115973
8.
Chen, L.; Hu, X.M.; Tian, W.; et al. Parallel planning: a new motion planning framework for autonomous driving. IEEE/CAA J. Autom. Sin., 2019, 6: 236−246. doi: 10.1109/JAS.2018.7511186
9.
Wang, F.Y.; Qin, R.; Li, J.J.; et al. Parallel societies: A computing perspective of social digital twins and virtual-real interactions. IEEE Trans. Comput. Soc. Syst., 2020, 7: 2−7. doi: 10.1109/TCSS.2020.2970305
10.
Yan, X.Q.; Jin, Z.X.; Han, F.S.; et al. Differentiable information bottleneck for deterministic multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024; pp. 27435–27444.
11.
Yan, X.Q.; Gan, Y.T.; Mao, Y.Q.; et al. Live and learn: Continual action clustering with incremental views. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, Vancouver, 20–27 February 2024; AAAI, 2024; pp. 16264–16271. doi: 10.1609/aaai.v38i15.29561
12.
Reynolds, C.W. Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Comput. Graphics, 1987, 21: 25−34. doi: 10.1145/37402.37406
13.
Helbing, D.; Molnár, P. Social force model for pedestrian dynamics. Phys. Rev. E, 1995, 51: 4282−4286. doi: 10.1103/PhysRevE.51.4282
14.
Helbing, D.; Farkas, I.; Vicsek, T. Simulating dynamical features of escape panic. Nature, 2000, 407: 487−90. doi: 10.1038/35035023
15.
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, Pasadena, 19–23 May 2008; IEEE: New York, 2008; pp. 1928–1935. doi: 10.1109/ROBOT.2008.4543489.
16.
van den Berg, J.; Guy, S.J.; Lin, M.; et al. Reciprocal n-body collision avoidance. In Robotics Research; Pradalier, C.; Siegwart, R.; Hirzinger, G., Eds.; Springer: Berlin/Heidelberg, 2011; pp. 3–19. doi: 10.1007/978-3-642-19457-3_1.
17.
Hughes, R.; Ondřej, J.; Dingliana, J. Holonomic collision avoidance for virtual crowds. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Copenhagen, 21–23 July 2014; Eurographics Association: Goslar, 2014; pp. 103–111.
18.
Golas, A.; Narain, R.; Curtis, S.; et al. Hybrid long-range collision avoidance for crowd simulation. IEEE Trans. Visualization Comput. Graphics, 2014, 20: 1022−1034. doi: 10.1109/TVCG.2013.235
19.
He, L.; Pan, J.; Narang, S.; et al. Dynamic group behaviors for interactive crowd simulation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Zurich, 11–13 July 2016; Eurographics Association: Goslar, 2016; pp. 139–147.
20.
Sud, A.; Andersen, E.; Curtis, S.; et al. Real-time path planning in dynamic virtual environments using multiagent navigation graphs. IEEE Trans. Visualization Comput. Graphics, 2008, 14: 526−538. doi: 10.1109/TVCG.2008.27
21.
Patil, S.; van den Berg, J.; Curtis, S.; et al. Directing crowd simulations using navigation fields. IEEE Trans. Visualization Comput. Graphics, 2011, 17: 244−254. doi: 10.1109/TVCG.2010.33
22.
Kretz, T.; Große, A.; Hengst, S.; et al. Quickest paths in simulations of pedestrians. Adv. Complex Syst., 2011, 14: 733−759. doi: 10.1142/S0219525911003281
23.
van Toll, W.G.; Cook IV, A.F.; Geraerts, R. Real-time density-based crowd simulation. Comput. Anim. Virtual Worlds, 2012, 23: 59−69. doi: 10.1002/cav.1424
24.
Zhao, G.Y.; Li, Y.T.; Xu, Q.R. From emotion AI to cognitive AI. Int. J. Netw. Dyn. Intell., 2022, 7: 65−72. doi: 10.53941/ijndi0101006
25.
Guy, S.J.; Kim, S.; Lin, M.C.; et al. Simulating heterogeneous crowd behaviors using personality trait theory. In Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Vancouver, 5–7 August 2011; ACM: New York, 2011; pp. 43–52. doi: 10.1145/2019406.2019413.
26.
Kim, S.; Guy, S.J.; Manocha, S.; et al. Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Costa Mesa, 9–11 March 2012; ACM: New York, 2012; pp. 55–62. doi: 10.1145/2159616.2159626.
27.
Goldberg, L.R. An alternative “description of personality”: The big-five factor structure. J. Pers. Soc. Psychol., 1990, 59: 1216−1229. doi: 10.1037/0022-3514.59.6.1216
28.
Durupinar, F.; Pelechano, N.; Allbeck, J.; et al. How the ocean personality model affects the perception of crowds. IEEE Comput. Graphics Appl., 2011, 31: 22−31. doi: 10.1109/MCG.2009.105
29.
Pelechano, N.; Allbeck, J.M.; Badler, N.I. Controlling individual agents in high-density crowd simulation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, San Diego, 2–4 August 2007; Eurographics Association: Goslar, 2007; pp. 99–108.
30.
Loscos, C.; Marchal, D.; Meyer, A. Intuitive crowd behavior in dense urban environments using local laws. In Proceedings of the Theory and Practice of Computer Graphics, Birmingham, 5 June 2003; IEEE: New York, 2003; pp. 122–129. doi: 10.1109/TPCG.2003.1206939.
31.
Sarmady, S.; Haron, F.; Talib, A.Z.H. Modeling groups of pedestrians in least effort crowd movements using cellular automata. In 2009 Third Asia International Conference on Modelling & Simulation, Bundang, 25–29 May 2009; IEEE: New York, 2009; pp. 520–525. doi: 10.1109/AMS.2009.16.
32.
Bruneau, J.; Pettré, J. Energy-efficient mid-term strategies for collision avoidance in crowd simulation. In Proceedings of the 14th ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Los Angeles, 7–9 August 2015, ACM: New York, 2015; pp. 119–127. doi: 10.1145/2786784.2786804.
33.
Guy, S.J.; Chhugani, J.; Kim, C.; et al. ClearPath: Highly parallel collision avoidance for multi-agent simulation. In Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, New Orleans, 1–2 August 2009; ACM: New York, 2009; pp. 177–187. doi: 10.1145/1599470.1599494.
34.
Guy, S.J.; Chhugani, J.; Curtis, S.; et al. PLEdestrians: A least-effort approach to crowd simulation. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Madrid, 2–4 July 2010, Eurographics Association: Goslar, 2010; pp. 119–128.
35.
Karamouzas, I.; Overmars, M. Simulating and evaluating the local behavior of small pedestrian groups. IEEE Trans. Visualization Comput. Graphics, 2012, 18: 394−406. doi: 10.1109/TVCG.2011.133
36.
Lemercier, S.; Auberlet, J.M. Towards more behaviours in crowd simulation. Comput. Anim. Virtual Worlds, 2016, 27: 24−34. doi: 10.1002/cav.1629
37.
Xiao, J.; Michalewicz, Z.; Zhang, L.X.; et al. Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evol. Comput., 1997, 1: 18−28. doi: 10.1109/4235.585889
38.
Orozco-Rosas, U.; Montiel, O.; Sepúlveda, R. Mobile robot path planning using membrane evolutionary artificial potential field. Appl. Soft Comput., 2019, 77: 236−251. doi: 10.1016/j.asoc.2019.01.036
39.
Dian, S.Y.; Zhong, J.N.; Guo, B.; et al. A smooth path planning method for mobile robot using a bes-incorporated modified QPSO algorithm. Expert Syst. Appl., 2022, 208: 118256. doi: 10.1016/j.eswa.2022.118256
40.
He, L.; van den Berg, J. Meso-scale planning for multi-agent navigation. In 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, 6–10 May 2013; IEEE: New York, 2013; pp. 2839–2844. doi: 10.1109/ICRA.2013.6630970.
41.
Geraerts, R. Planning short paths with clearance using explicit corridors. In 2010 IEEE International Conference on Robotics and Automation, Anchorage, 3–7 May 2010; IEEE: New York, pp. 1997–2004, 2010. doi: 10.1109/ROBOT.2010.5509263.
42.
Shi, Y.P.; Zhang, G.J.; Lu, D.J.; et al. Intervention optimization for crowd emotional contagion. Inf. Sci., 2021, 576: 769−789. doi: 10.1016/j.ins.2021.08.056
43.
Moussaïd, M.; Kapadia, M.; Thrash, T.; et al. Crowd behaviour during high-stress evacuations in an immersive virtual environment. J. R. Soc. Interface, 2016, 13: 20160414. doi: 10.1098/rsif.2016.0414
44.
Kapadia, M.; Pelechano, N.; Allbeck, J.; et al. Virtual Crowds: Steps Toward Behavioral Realism; Springer: Cham, 2016. doi: 10.1007/978-3-031-02586-0
45.
Helbing, D.; Buzna, L.; Johansson, A.; et al. Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Trans. Sci., 2005, 39: 1−24. doi: 10.1287/trsc.1040.0108
46.
Durupinar, F.; Kapadia, M.; Deutsch, S.; et al. PERFORM: Perceptual approach for adding OCEAN personality to human motion using laban movement analysis. ACM Trans. Graphics, 2016, 36: 6. doi: 10.1145/2983620
47.
Durupınar, F.; Güdükbay, U.; Aman, A.; et al. Psychological parameters for crowd simulation: From audiences to mobs. IEEE Trans. Vis. Comput. Graph., 2016, 22: 2145−2159. doi: 10.1109/TVCG.2015.2501801
48.
Lv, P.; Yu, Q.Q.; Xu, B.Y.; et al. Emotional contagion-aware deep reinforcement learning for antagonistic crowd simulation. IEEE Trans. Affect. Comput., 2023, 14: 2939−2953. doi: 10.1109/TAFFC.2022.3225037
49.
Xu, M.L.; Li, C.C.; Lv, P.; et al. Emotion-based crowd simulation model based on physical strength consumption for emergency scenarios. IEEE Trans. Intell. Transp. Syst., 2021, 22: 6977−6991. doi: 10.1109/TITS.2020.3000607
50.
Mao, Y.; Yang, S.W.; Li, Z.N.; et al. Personality trait and group emotion contagion based crowd simulation for emergency evacuation. Multimed. Tools Appll., 2020, 79: 3077−3104. doi: 10.1007/s11042-018-6069-3
51.
Liu, H.; Lu, D.J.; Zhang, G.J.; et al. Recurrent emotional contagion for the crowd evacuation of a cyber-physical society. Inf. Sci., 2021, 575: 155−172. doi: 10.1016/j.ins.2021.06.036
52.
Xu, T.F.; Shi, D.D.; Chen, J.; et al. Dynamics of emotional contagion in dense pedestrian crowds. Phys. Lett. A, 2020, 384: 126080. doi: 10.1016/j.physleta.2019.126080
53.
Coleman, J.S.; James, J. The equilibrium size distribution of freely-forming groups. Sociometry, 1961, 24: 36−45. doi: 10.2307/2785927
54.
Gorrini, A.; Bandini, S.; Sarvi, M. Group dynamics in pedestrian crowds: Estimating proxemic behavior. Transp. Res. Rec.: J. Transp. Res. Board, 2014, 2421: 51−56. doi: 10.3141/2421-06
55.
Rodriguez, A.; Laio, A. Clustering by fast search and find of density peaks. Science, 2014, 344: 1492−1496. doi: 10.1126/science.1242072
56.
Wu, Y.P.; Ye, Y.D.; Zhao, C.Y.; et al. Collective density clustering for coherent motion detection. IEEE Trans. Multim., 2018, 20: 1418−1431. doi: 10.1109/TMM.2017.2771477
57.
Schulz, N.; Vogelhuber, V. Horde3d - next-generation graphics engine. 2006. Available online: http://www.horde3d.org/.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us