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Priority driven Local Optimization for Crowd Simulation
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).ORCID iD: 0000-0002-7257-0761
2019 (English)In: AAMAS '19 Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019, p. 2180-2182Conference paper, Published paper (Refereed)
Abstract [en]

We provide an initial model and preliminary findings of a lookahead based local optimization scheme for collision resolution between agents in large goal-directed crowd simulations. Considering crowd simulation to be a global optimization problem, we break down this large problem into smaller problems where each potential collision resolution step is independently optimized in terms of a criticality measure. Agents resolved earlier in order of criticality, maintain the optimized velocity obtained, for the resolution of agents that come later in that order. Hence, the problem is converted to a low dimensional optimization problem of one or two agents where all other obstacles are static or deterministically dynamic. We illustrate the performance of our method on four well known test scenarios.

Place, publisher, year, edition, pages
2019. p. 2180-2182
National Category
Engineering and Technology
Research subject
Computer Science; Human-computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-259706ISBN: 978-1-4503-6309-9 (print)OAI: oai:DiVA.org:kth-259706DiVA, id: diva2:1353125
Conference
Autonomous Agents and Multi-Agent Systems (AAMAS), Montreal QC, Canada — May 13 - 17, 2019
Note

QC 20191011

Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-10-11Bibliographically approved

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Saikia, HimangshuYang, FangkaiPeters, Christopher

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