Representative Synthetic Crowds for Inclusive Environment Design

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dc.contributor.authorHaworth, Brandon
dc.contributor.authorKapadia, Mubbasir
dc.contributor.authorFaloutsos, Petros
dc.date.accessioned2023-05-28T15:18:52Z
dc.date.available2023-05-28T15:18:52Z
dc.date.copyright2021en_US
dc.date.issued2021-12-22
dc.description.abstractSynthetic crowds serve as a powerful tool for numerous applications across industry and research. We argue that, while synthetic crowds may also be a highly valuable tool for predictive and inclusive building design, the current state-of-the-art is not representative enough to safely impact this space. We show that model fidelity and non-biomechanical rules can not capture the non-linear kinematics of both normative and non-normative gaits. Finally, we argue that recent developments in deep reinforcement learning may afford a significant increase in fidelity and a move away from limited data driven methods, ad hoc or expert rules, and heuristics. These new approaches, however, also have several issues that are the focus of current research and could one day serve as the groundwork for high fidelity inclusive design processes driven by simulation.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThe research was supported in part by NSERC Create DAV, ORF/ISSUM, NSERC Discovery [funding reference number RGPIN-2021-03541], and NSF awards: IIS-1703883, IIS-1955404, IIS-1955365, RETTL-2119265, and EAGER-2122119.en_US
dc.identifier.citationHaworth, B., Kapadia, M., & Faloutsos, P. (2021). Representative Synthetic Crowds for Inclusive Environment Design. 2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), 150–153. https://doi.org/10.1109/AIVR52153.2021.00035en_US
dc.identifier.urihttp://hdl.handle.net/1828/15133
dc.language.isoenen_US
dc.publisher2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)en_US
dc.subjectSynthetic Crowds
dc.subjectHuman Movement Simulation
dc.subjectInclusive Environment Design
dc.subject.departmentDepartment of Computer Science
dc.titleRepresentative Synthetic Crowds for Inclusive Environment Designen_US
dc.typePostprinten_US

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