Modeling Gap Seeking Behaviors for Agent-based Crowd Simulation

Luo, Linbo; Chai, Cheng; Zhou, Suiping; Ma, Jianfeng

doi:10.1145/2915926.2915944

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The key contributions of this paper are summarized as follows: We demonstrate the applicability of mapping intuitive proactive steering strategies of individuals observed from real‐world scenarios into various behaviour models and using a unified framework to drive the behaviour selection and execution of these behaviours. With the focus on intersecting crowd scenarios, we identify two inter‐related proactive steering behaviours, namely, gap‐seeking and following behaviours and propose the detailed behaviour models for them. The gap‐seeking behaviour model extends our previous work [LCZM16] by considering the detected gap as a dynamic moving object when the moving direction for gap seeking is computed. Related to gap seeking, the following behaviour model not only controls how an agent performs following motion, but also dynamically determines suitable followee (i.e.…”

Section: Introductionmentioning

confidence: 78%

“…r With the focus on intersecting crowd scenarios, we identify two inter-related proactive steering behaviours, namely, gap-seeking and following behaviours and propose the detailed behaviour models for them. The gap-seeking behaviour model extends our previous work [LCZM16] by considering the detected gap as a dynamic moving object when the moving direction for gap seeking is computed. Related to gap seeking, the following behaviour model not only controls how an agent performs following motion, but also dynamically determines suitable followee (i.e.…”

Section: Introductionmentioning

confidence: 82%

See 1 more Smart Citation

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

Luo

Chai

et al. 2017

Computer Graphics Forum

Self Cite

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How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.

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Section: Introductionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 82%

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

Luo

Chai

et al. 2017

Computer Graphics Forum

Self Cite

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“…Crowd simulation models assist in predicting the crowd threats and in doing so reduce the deaths that may occur [1]. Many researchers had focused on the interaction of human behaviour, characteristics and phenomena in a complex geometrical environment to find the most common variables that led to the death or injury [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Investigating pedestrians’ obstacle avoidance behaviour

et al. 2020

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Modelling and simulating pedestrian motions are standard ways to investigate crowd dynamics aimed to enhance pedestrians’ safety. Movement of people is affected by interactions with one another and with the physical environment that it may be a worthy line of research. This paper studies the impact of speed on how pedestrians respond to the obstacles (i.e. Obstacles avoidance behaviour). A field experiment was performed in which a group of people were instructed to perform some obstacles avoidance tasks at two levels of normal and high speeds. Trajectories of the participants are extracted from the video recordings for the subsequent intentions:(i) to seek out the impact of total speed, x and yaxis (ii) to observe the impact of the speed on the movement direction, x-axis, (iii) to find out the impact of speed on the lateral direction, y-axis. The results of the experiments could be used to enhance the current pedestrian simulation models.

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Agent-based crowd simulation: an in-depth survey of determining factors for heterogeneous behavior

Khan,

Deng

2024

Vis Comput

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Modeling Gap Seeking Behaviors for Agent-based Crowd Simulation

Cited by 5 publications

References 22 publications

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

Investigating pedestrians’ obstacle avoidance behaviour

Agent-based crowd simulation: an in-depth survey of determining factors for heterogeneous behavior

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