Aggregate dynamics for dense crowd simulation

Narain, Rahul; Golas, Abhinav; Curtis, Sean; Lin, Ming C.

doi:10.1145/1661412.1618468

Cited by 165 publications

(141 citation statements)

References 33 publications

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“…Such approaches include, cellular automata [16], social forces [17][18][19], vision-based [20], continuum-based [21], velocity-obstacle-based [22,23], and rule based [24]. All of these models are compatible with the plan adaptation abstraction.…”

Section: Plan Adaptationmentioning

confidence: 99%

Menge: A Modular Framework for Simulating Crowd Movement

2016

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We present Menge, a cross-platform, extensible, modular framework for simulating pedestrian movement in a crowd. Menge's architecture is inspired by an implicit decomposition of the problem of simulating crowds into component subproblems. These subproblems can typically be solved in many ways; different combinations of subproblem solutions yield crowd simulators with likewise varying properties. Menge creates abstractions for those subproblems and provides a plug-in architecture so that a novel simulator can be dynamically configured by connecting built-in and bespoke implementations of solutions to the various subproblems. Use of this type of framework could facilitate crowd simulation research, evaluation, and applications by reducing the cost of entering the domain, facilitating collaboration, and making comparisons between algorithms simpler. We show how the Menge framework is compatible with many prior models and algorithms used in crowd simulation and illustrate its flexibility via a varied set of scenarios and applications.

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Section: Plan Adaptationmentioning

confidence: 99%

Menge: A Modular Framework for Simulating Crowd Movement

2016

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“…For crowd navigation, macroscopic navigation models such as [16,25] that regard the crowd as a whole are investigated as an alternative to the agent-based microscopic navigation models [4,17,18]. Macroscopic models increased the agent count at the expense of lowering the navigation quality.…”

Section: Related Workmentioning

confidence: 99%

Real-time collision-free linear trajectory generation on GPU for crowd simulations

Barut

Haciömeroğlu

2015

Vis Comput

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Crowd simulations are mostly employed to compose a background in the current scene. For such ambient crowds, it might be unnecessary to perform complex steering calculations. In this study, a steering-free crowd simulation which eliminates the computational cost arising from expensive steering maneuvers is presented. For this purpose, agents are assigned a linear trajectory that is guaranteed to be collision free before entering the simulation. These trajectories are calculated using readily available rendering pipeline of the GPU. To that end, existing agents' bounding discs are rendered in a spatio-temporal manner as each one forms a straight 3D tube and a projection from a selected initial position is captured. Using the blank areas (holes) in this image, it is possible to determine a suitable constant velocity (a goal position and a speed). In experiments, we not only assess three different methods to choose one of the candidate solutions, but also compare our approach with an existing work. Test results reveal that our technique gives better results in both populating an empty environment with agents quicker and reaching a higher maximum number of agents than the existing method.

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“…4 Simulation of congestion during the Hajj. Photo taken from Narain et al (2009) to anticipate other situations. The movement of a group can be used in applications, for example, the movement of pilgrim swarms (Fig.…”

Section: Prediction and Forecastingmentioning

confidence: 99%

An introduction to swarming robotics: application development trends

2013

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Animals help to sustain the environment's life cycle and ecosystem. Without human intervention, these creatures carry out their 'spontaneous routine' jobs and contribute towards balance in nature. Any natural system that congregates as a result of some form of collective intelligence of nature is also known as swarm intelligence (SI). This metaphor inspires a variety of techniques to solve the problem of calculating, in most cases dealing with optimization problems and has sparked interest amongst scientists. It is very trying for a new researcher to understand the whole concept of swarming robotics (SR) and optimization algorithm (i.e. realizing the idea from animal's perception to the SR application). In addition, the existing algorithms are computationally complicated, difficult to be understood by beginners as there are too many parameters. Thus, in this paper, we simplify the three branches of the main applications which are frequently used for SI namely: (1) optimization and networks design, (2) prediction and forecasting, and (3) SR. This paper summarizes the basic understanding overview of swarming robotics and discusses their basic concepts and principles.

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Aggregate dynamics for dense crowd simulation

Cited by 165 publications

References 33 publications

Menge: A Modular Framework for Simulating Crowd Movement

Menge: A Modular Framework for Simulating Crowd Movement

Real-time collision-free linear trajectory generation on GPU for crowd simulations

An introduction to swarming robotics: application development trends

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