Skip to main content

Follower behavior under stress in immersive VR


Understanding human decision making is a key requirement to improve crowd simulation models so that they can better mimic real human behavior. It is often difficult to study human decision making during dangerous situations because of the complexity of the scenarios and situations to be simulated. Immersive virtual reality offers the possibility to carry out such experiments without exposing participants to real danger. In the real world, it has often been observed that people tend to follow others in certain situations (e.g., unfamiliar environments or stressful situations). In this paper, we study human following behavior when it comes to exit choice during an evacuation of a train station. We have carried out immersive VR experiments under different levels of stress (alarm only or alarm plus fire), and we have observed how humans consistently tend to follow the crowd regardless of the levels of stress. Our results show that decision making is strongly influenced by the behavior of the virtual crowd: the more virtual people running, the more likely are participants to simply follow others. The results of this work could improve behavior simulation models during crowd evacuation, and thus build more plausible scenarios for training firefighters.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. Ahn J, Wang N, Thalmann D, Boulic R (2012) Within-crowd immersive evaluation of collision avoidance behaviors. In: Proceedings of the 11th ACM SIGGRAPH international conference on virtual-reality continuum and its applications in industry, pp 231–238. ACM

  2. Argelaguet F, Olivier A-H, Bruder G, Pettré J, Lécuyer (2015) A Virtual proxemics: locomotion in the presence of obstacles in large immersive projection environments. In VR

  3. Bruneau J, Olivier A-H, Pettre J (2015) Going through, going around: a study on individual avoidance of groups. IEEE Trans Vis Comput Gr 21(4):520–528

    Article  Google Scholar 

  4. Cassol VJ, Testa ES, Jung CR, Usman M, Faloutsos P, Berseth G, Kapadia M, Badler NI, Musse SR (2017) Evaluating and optimizing evacuation plans for crowd egress. IEEE Comput Gr Appl 37(4):60–71

    Article  Google Scholar 

  5. Cialdini RB (1993) Influence: the psychology of persuasion (Rev. ed.). Morrow, New York

  6. Coultas J C (2004) When in Rome... an evolutionary perspective on conformity. Gr Process Intergr Relat 7(4):317–331

    Article  Google Scholar 

  7. Ennis C, Peters C, O’Sullivan C (2008) Perceptual evaluation of position and orientation context rules for pedestrian formations. In: Proceedings of the 5th symposium on Applied perception in graphics and visualization, pp 75–82. ACM

  8. Ennis C, Peters C, O’Sullivan C (2011) Perceptual effects of scene context and viewpoint for virtual pedestrian crowds. ACM Trans Appl Percept (TAP) 8(2):10:1–10:22

    Google Scholar 

  9. Gonzalez-Franco M, Lanier J (2017) Model of illusions and virtual reality. Front Psychol 8:1125

    Article  Google Scholar 

  10. Gonzalez-Franco M, Slater M, Birney ME, Swapp D, Haslam SA, Reicher SD (2018) Participant concerns for the learner in a virtual reality replication of the milgram obedience study. PloS One 13(12):e0209704

    Article  Google Scholar 

  11. Gupta N, Singh A, Butail S (2017) The effect of instructional priming on postural responses to virtual crowds. In: Virtual humans and crowds for immersive environments (VHCIE), IEEE, 1–8

  12. Guy S J, Chhugani J, Curtis S, Dubey P, Lin M, Manocha D (2010) Pledestrians: a least-effort approach to crowd simulation. In: Proceedings of the 2010 ACM SIGGRAPH/Eurographics symposium on computer animation, Eurographics Association, 119–128

  13. Guy S J, Kim S, Lin M C, Manocha D (2010) Simulating heterogeneous crowd behaviors using personality trait theory. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics symposium on computer animation, pp 43–52. ACM

  14. Guy SJ, Van Den Berg J, Liu W, Lau R, Lin MC, Manocha D (2012) A statistical similarity measure for aggregate crowd dynamics. ACM Trans Graph (TOG) 31(6):190

    Article  Google Scholar 

  15. Haworth B, Usman M, Berseth G, Kapadia M, Faloutsos P (2015) Evaluating and optimizing level of service for crowd evacuations. In: Proceedings of the 8th ACM SIGGRAPH conference on motion in games, MIG ’15, pp 91–96, New York, NY, USA, ACM

  16. Kazdin AE, Kazdin AE (2000) Encyclopedia of psychology, vol 2. American Psychological Association, Washington

    Google Scholar 

  17. Knob P, Balotin M, Musse SR (2018) Simulating crowds with ocean personality traits. In: Proceedings of the 18th international conference on intelligent virtual agents, pp 233–238. ACM

  18. Kyriakou M, Chrysanthou Y (2018) How responsiveness, group membership and gender affect the feeling of presence in immersive virtual environments populated with virtual crowds. In: Proceedings of the 11th annual international conference on motion, interaction, and games, p 12. ACM

  19. Lerner A, Chrysanthou Y, Shamir A, Cohen-Or D (2010) Context-dependent crowd evaluation. Comput Graph. Forum 29(7):2197–2206

    Google Scholar 

  20. Llobera J, Spanlang B, Ruffini G, Slater M (2010) Proxemics with multiple dynamic characters in an immersive virtual environment. ACM Trans Appl Percept (TAP) 8(1):3

    Google Scholar 

  21. Maples-Keller JL, Yasinski CW, Manjin N, Rothbaum BO (2017) Virtual reality-enhanced extinction of phobias and post-traumatic stress. Neurotherapeutics 14:554–563

    Article  Google Scholar 

  22. Moussaïd M, Kapadia M, Thrash T, Sumner RW, Gross M, Helbing D, Hölscher C (2016) Crowd behaviour during high-stress evacuations in an immersive virtual environment. J R Soc Interface 13(122):20160414

    Article  Google Scholar 

  23. Narang, S, Best A, Randhavane T, Shapiro A, Manocha D (2016) Pedvr: simulating gaze-based interactions between a real user and virtual crowds. In: Proceedings of the 22nd ACM conference on virtual reality software and technology, pp 91–100. ACM, 2016

  24. Olivier A-H, Bruneau J, Cirio G, Pettré J (2014) A virtual reality platform to study crowd behaviors. Transp Res Procedia 2:114–122

    Article  Google Scholar 

  25. Olivier A-H, Marin A, Crétual A, Berthoz A, Pettré J (2013) Collision avoidance between two walkers: role-dependent strategies. Gait Posture 38(4):751–756

    Article  Google Scholar 

  26. Ondřej J, Pettré J, Olivier A-H, Donikian S (2010) A synthetic-vision based steering approach for crowd simulation. ACM Trans Gr (TOG) 29(4):123

    Google Scholar 

  27. Pelechano N, Allbecky JM (2016) Feeling crowded yet?: crowd simulations for VR. In: Virtual humans and crowds for immersive environments (VHCIE), pp 17–21. IEEE

  28. Pelechano N, Badler NI (2006) Modeling crowd and trained leader behavior during building evacuation. IEEE Comput Gr Appl 26(6):80–86

    Article  Google Scholar 

  29. Pelechano N, Stocker C, Allbeck J, Badler N (2008) Being a part of the crowd: towards validating vr crowds using presence. In: Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, vol 1, p 136–142

  30. Pelechano Gómez N, Stocker C, Allbeck J, Badler N (2007) Feeling crowded? exploring presence in virtual crowds. In: Proceedings of PRESENCE 2007, pp 373–376

  31. Ríos A, Palomar M, Pelechano N (2018) Users’ locomotor behavior in collaborative virtual reality. In: Proceedings of the 11th annual international conference on motion, interaction, and games, pp 15. ACM

  32. Rojas FA, Yang HS (2013) Immersive human-in-the-loop hmd evaluation of dynamic group behavior in a pedestrian crowd simulation that uses group agent-based steering. In: Proceedings of the 12th ACM SIGGRAPH international conference on virtual-reality continuum and its applications in industry, VRCAI ’13, pp 31–40, New York, NY, USA, ACM

  33. Ríos A, Pelechano N (2018) Follower behavior in a virtual environment. In: Virtual humans and crowds in immersive environments (VHCIE), March

  34. Sohre N, Mackin C, Interrante V, Guy SJ (2017) Evaluating collision avoidance effects on discomfort in virtual environments. In: Virtual humans and crowds for immersive environments (VHCIE), pp 1–5. IEEE

  35. Steed A, Pan Y, Watson Z, Slater M (2018) “We wait”—the impact of character responsiveness and self embodiment on presence and interest in an immersive news experience. Front Robot AI 5:112

    Article  Google Scholar 

  36. Turner A, Penn A (2002) Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment. Environ Plan B Plan Des 29(4):473–490

    Article  Google Scholar 

  37. Turner A, Penn A (2007) Evolving direct perception models of human behavior in building systems. In: Pedestrian and Evacuation Dynamics 2005. Springer, Berlin, Heidelberg, pp 411–422

  38. Van Toll WG, Cook AF, Geraerts R (2012) Real-time density-based crowd simulation. Comput Anim Virtual Worlds 23(1):59–69

    Article  Google Scholar 

  39. Wolinski D, Guy SJ, Olivier A-H, Lin M, Manocha D, Pettré J (2014) Parameter estimation and comparative evaluation of crowd simulations. Comput Graph Forum 33(2):303–312

    Article  Google Scholar 

Download references


This work was partly funded by the Spanish Ministry of Economy, Industry and Competitiveness under Grant No. TIN2017-88515-C2-1-R.

Author information



Corresponding author

Correspondence to Alejandro Ríos.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ríos, A., Pelechano, N. Follower behavior under stress in immersive VR. Virtual Reality 24, 683–694 (2020).

Download citation


  • Crowd following
  • Immersive VR
  • Studies of human behavior