Martin, Rafael F.Parisi, Daniel2020-06-262020-06-262020-020925-2312http://ri.itba.edu.ar/handle/123456789/2230"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."enPEATONESDINAMICASIMULACIONNAVEGACIONREDES NEURONALESINTELIGENCIA ARTIFICIALArtículos de Publicaciones Periódicas