Dynamic combination of crowd steering policies based on context

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• dc.contributor.author Cabrero Daniel, Beatriz
• dc.contributor.author Marques, Ricardo
• dc.contributor.author Hoyet, Ludovic
• dc.contributor.author Pettré, Julien
• dc.contributor.author Blat, Josep
• dc.date.accessioned 2024-02-07T07:09:29Z
• dc.date.issued 2022
• dc.description.abstract Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this paper, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context.
• dc.format.mimetype application/pdf
• dc.identifier.citation Cabrero-Daniel B, Marques R, Hoyet L, Pettré J, Blat J. Dynamic combination of crowd steering policies based on context. Comput Graph Forum. 2022;41(2):209-19. DOI: 10.1111/cgf.14469
• dc.identifier.doi http://dx.doi.org/10.1111/cgf.14469
• dc.identifier.issn 0167-7055
• dc.identifier.uri http://hdl.handle.net/10230/58977
• dc.language.iso eng
• dc.publisher Wiley
• dc.relation.ispartof Computer Graphics Forum. 2022;41(2):209-19
• dc.rights This is the peer reviewed version of the following article: Cabrero-Daniel B, Marques R, Hoyet L, Pettré J, Blat J. Dynamic combination of crowd steering policies based on context. Comput Graph Forum. 2022;41(2):209-19, which has been published in final form at http://dx.doi.org/10.1111/cgf.14469. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword CCS Concepts
• dc.subject.keyword Motion path planning
• dc.subject.keyword Agent / discrete models
• dc.subject.keyword Multi-agent systems
• dc.title Dynamic combination of crowd steering policies based on context
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion