Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network

"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 o...

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Autores principales: Martin, Rafael F., Parisi, Daniel
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
PEATONES
DINAMICA
SIMULACION
NAVEGACION
REDES NEURONALES
INTELIGENCIA ARTIFICIAL
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/2230
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-2230
record_format dspace
spelling I32-R138-123456789-22302022-12-07T13:06:13Z Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network Martin, Rafael F. Parisi, Daniel PEATONES DINAMICA SIMULACION NAVEGACION REDES NEURONALES INTELIGENCIA ARTIFICIAL "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." 2020-06-26T20:35:59Z 2020-06-26T20:35:59Z 2020-02 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 0925-2312 http://ri.itba.edu.ar/handle/123456789/2230 en info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2019.10.062 info:eu-repo/grantAgreement/PID/2015-0003/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/ITBACyT/2018-42/AR. Ciudad Autónoma de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic PEATONES
DINAMICA
SIMULACION
NAVEGACION
REDES NEURONALES
INTELIGENCIA ARTIFICIAL
spellingShingle PEATONES
DINAMICA
SIMULACION
NAVEGACION
REDES NEURONALES
INTELIGENCIA ARTIFICIAL
Martin, Rafael F.
Parisi, Daniel
Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
topic_facet PEATONES
DINAMICA
SIMULACION
NAVEGACION
REDES NEURONALES
INTELIGENCIA ARTIFICIAL
description "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."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Martin, Rafael F.
Parisi, Daniel
author_facet Martin, Rafael F.
Parisi, Daniel
author_sort Martin, Rafael F.
title Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
title_short Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
title_full Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
title_fullStr Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
title_full_unstemmed Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
title_sort data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/2230
work_keys_str_mv AT martinrafaelf datadrivensimulationofpedestriancollisionavoidancewithanonparametricneuralnetwork
AT parisidaniel datadrivensimulationofpedestriancollisionavoidancewithanonparametricneuralnetwork
_version_ 1765660767245303808

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