Dynamic Group Behaviors for Interactive Crowd Simulation

He, Liang; Pan, Jia; Narang, Sahil; Wang, Wenping; Manocha, Dinesh

doi:10.48550/arxiv.1602.03623

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…This model has been implemented on the GPU [5] and has shown credible speedup over the multi-core CPU implementation through use of hashing instead of naive nearest neighbor search. Group behavior has also been included in VO models [13,30] allowing people to be joined into groups. Such people attempt to remain close to other members of the group and aim for the same goal location.…”

Section: Introductionmentioning

confidence: 99%

Fast Simulation of Crowd Collision Avoidance

Charlton

Gonzalez

Maddock

et al. 2019

Advances in Computer Graphics

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0000−0001−8402−6723] , Luis Rene Montana Gonzalez 1[0000−0002−0511−0466] , Steve Maddock 1[0000−0003−3179−0263] , and Paul Richmond 1[0000−0002−4657−5518]Abstract. Real-time large-scale crowd simulations with realistic behavior, are important for many application areas. On CPUs, the ORCA pedestrian steering model is often used for agent-based pedestrian simulations. This paper introduces a technique for running the ORCA pedestrian steering model on the GPU. Performance improvements of up to 30 times greater than a multi-core CPU model are demonstrated. This improvement is achieved through a specialized linear program solver on the GPU and spatial partitioning of information sharing. This allows over 100,000 people to be simulated in real time (60 frames per second).

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Section: Introductionmentioning

confidence: 99%

Fast Simulation of Crowd Collision Avoidance

Charlton

Gonzalez

Maddock

et al. 2019

Advances in Computer Graphics

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show abstract

“…This model has been implemented on the GPU [2] and has shown credible speedup over the multi-core CPU implementation through use of hashing instead of naive nearest neighbor search. Group behavior has also been included in VO models [23,24] allowing people to be joined into groups. Such people attempt to remain close to other members of the group and aim for the same goal location.…”

Section: Introductionmentioning

confidence: 99%

Simulating Crowds and Autonomous Vehicles

Charlton

Gonzalez

Maddock

et al. 2020

Lecture Notes in Computer Science

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Understanding how people view and interact with autonomous vehicles is important to guide future directions of research. One such way of aiding understanding is through simulations of virtual environments involving people and autonomous vehicles. We present a simulation model that incorporates people and autonomous vehicles in a shared urban space. The model is able to simulate many thousands of people and vehicles in real-time. This is achieved by use of GPU hardware, and through a novel linear program solver optimized for large numbers of problems on the GPU. The model is up to 30 times faster than the equivalent multi-core CPU model.

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Dynamic Group Behaviors for Interactive Crowd Simulation

Cited by 2 publications

References 23 publications

Fast Simulation of Crowd Collision Avoidance

Fast Simulation of Crowd Collision Avoidance

Simulating Crowds and Autonomous Vehicles

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