Data-parallel implementation of the CPD method.

Data-parallel implementation of the CPD method.

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Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared with...

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... compute flow of the method is presented in Figure 4: we encode the entities as geometric primitives and use the GPU's depth buffer to quickly obtain the PSM tessellation as discrete pixels, obtaining a shared data structure that allows each entity to compute its relative centroid and resulting penalty forces in a dataparallel fashion. The entities do not need to conduct a costly nearest neighbor search, as they simply consume and interact with the set of pixels representing their PS in the PSM. ...
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... the absence of any distracted pedestrians, only a handful of instances of high collision likelihood have been observed. The count increases exponentially as the ratio of distracted entities increases within the dense crowd, as seen in Figure 14. These collision counts were also inversely proportional to corridor width; not due to increased bidirectional flow density, but rather due to the lack of additional space for undistracted pedestrians to perform their avoidance maneuvers. ...

Citations

... Mobility model: these papers present an innovation in terms of mobility modeling. Two papers in this category (Prédhumeau et al. 2021, Hesham andWainer 2021) deal with pedestrian mobility: their objective is to propose an improved model to simulate pedestrian behavior. A third paper, i.e. ...
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... Most crowd models are based on assumptions, intuition and literature and many reflect specific scenarios (Dubroca-Voisin et al., 2019;Li et al., 2015). Crowd modelling involves simulating real-world crowds based on scientific hypotheses related to social, physical, biological and psychological factors (Bellomo, Clarke, Gibelli, Townsend, & Vreugdenhil, 2016;Hesham & Wainer, 2021;Zhang et al., 2018). For example, when looking at a crowd as a collective movement and studying its behaviour as a physical (e.g., fluid) or biological (e.g., animal swarm) phenomena including basic interactions, crowd models can describe real-world events using mathematical processes. ...
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... In recent years, scholars have conducted extensive and indepth research on crowd evacuation. [1][2][3][4][5] For example, for the evacuation of dense crowds, Hesham and Wainer 1 presented advanced models based on centroidal particle dynamics (CPD), an agent-based short-range collisionavoidance model for pedestrians in dense crowds. Their models can reproduce visually convincing emergent crowd phenomena. ...
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Crowd simulation demands careful consideration in regard to the classic trade-off between accuracy and efficiency. Particle-based methods have seen success in various applications in architecture, military, urban planning, and entertainment. This method focuses on local dynamics of individuals in large crowds, with a focus on serious games and entertainment. The technique uses an area-based penalty force that captures the infringement of each entity's personal space. This method does not need a costly nearest-neighbor search and allows for an inherently data-parallel implementation capable of simulating thousands of entities at interactive frame rates. The algorithm reproduces personal space compression around motion barriers for moving crowds and around points of interest for static crowds.