Fast Simulation of Crowd Collision Avoidance

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. Pe...

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Bibliographic Details
Main Authors: Charlton, John, Gonzalez, Luis Rene Montana, Maddock, Steve, Richmond, Paul
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2019
Subjects:
Robotics cs.RO
FOS Computer and information sciences
Orca
Online Access:https://dx.doi.org/10.48550/arxiv.1908.10107
https://arxiv.org/abs/1908.10107
id ftdatacite:10.48550/arxiv.1908.10107
record_format openpolar
spelling ftdatacite:10.48550/arxiv.1908.10107 2023-05-15T17:53:23+02:00 Fast Simulation of Crowd Collision Avoidance Charlton, John Gonzalez, Luis Rene Montana Maddock, Steve Richmond, Paul 2019 https://dx.doi.org/10.48550/arxiv.1908.10107 https://arxiv.org/abs/1908.10107 unknown arXiv https://dx.doi.org/10.1007/978-3-030-22514-8_22 Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 CC-BY-NC-SA Robotics cs.RO FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2019 ftdatacite https://doi.org/10.48550/arxiv.1908.10107 https://doi.org/10.1007/978-3-030-22514-8_22 2022-03-10T16:29:05Z 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). : 12 pages, 6 figures, 36th Computer Graphics International Conference (CGI 2019) Article in Journal/Newspaper Orca DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Robotics cs.RO
FOS Computer and information sciences
spellingShingle Robotics cs.RO
FOS Computer and information sciences
Charlton, John
Gonzalez, Luis Rene Montana
Maddock, Steve
Richmond, Paul
Fast Simulation of Crowd Collision Avoidance
topic_facet Robotics cs.RO
FOS Computer and information sciences
description 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). : 12 pages, 6 figures, 36th Computer Graphics International Conference (CGI 2019)
format Article in Journal/Newspaper
author Charlton, John
Gonzalez, Luis Rene Montana
Maddock, Steve
Richmond, Paul
author_facet Charlton, John
Gonzalez, Luis Rene Montana
Maddock, Steve
Richmond, Paul
author_sort Charlton, John
title Fast Simulation of Crowd Collision Avoidance
title_short Fast Simulation of Crowd Collision Avoidance
title_full Fast Simulation of Crowd Collision Avoidance
title_fullStr Fast Simulation of Crowd Collision Avoidance
title_full_unstemmed Fast Simulation of Crowd Collision Avoidance
title_sort fast simulation of crowd collision avoidance
publisher arXiv
publishDate 2019
url https://dx.doi.org/10.48550/arxiv.1908.10107
https://arxiv.org/abs/1908.10107
genre Orca
genre_facet Orca
op_relation https://dx.doi.org/10.1007/978-3-030-22514-8_22
op_rights Creative Commons Attribution Non Commercial Share Alike 4.0 International
https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
cc-by-nc-sa-4.0
op_rightsnorm CC-BY-NC-SA
op_doi https://doi.org/10.48550/arxiv.1908.10107
https://doi.org/10.1007/978-3-030-22514-8_22
_version_ 1766161091856957440

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