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dc.contributor.authorMartin, Rafael F.
dc.contributor.authorParisi, Daniel R.
dc.date.accessioned2020-06-26T20:35:59Z
dc.date.available2020-06-26T20:35:59Z
dc.date.issued2020
dc.identifier.issn0925-2312
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/2230
dc.description.abstract"Data-driven simulation of pedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networks, which does not have to deal with a huge number of free parameters as in the case of multilayer neural networks. Although the method is general, we focus on the one pedestrian - one obstacle problem. Experimental data were collected in a motion capture laboratory providing high-precision trajectories. The proposed model allows us to simulate the trajectory of a pedestrian avoiding an obstacle from any direction. Together with the methodology specifications, we provide the data set needed for performing the simulations of this kind of pedestrian dynamic system."en
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2019.10.062
dc.relation
dc.relationinfo:eu-repo/grantAgreement/PID2015-003/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ITBACyT-2018- 42/AR. Ciudad Autónoma de Buenos Aires
dc.subjectPEATONES
dc.subjectDINAMICA
dc.subjectSIMULACION
dc.subjectNAVEGACION
dc.subjectREDES NEURONALES
dc.subjectINTELIGENCIA ARTIFICIAL
dc.titleData-driven simulation of pedestrian collision avoidance with a nonparametric neural networken
dc.typeArtículos de Publicaciones Periódicases
dc.typeinfo:eu-repo/semantics/acceptedVersion
itba.description.filiationFil: Martin, Rafael F. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiationFil: Parisi, Daniel R. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiationFil: Parisi, Daniel R. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.


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