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.
@article{arxiv.1907.07702,
title = {Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network},
author = {Rafael F. Martin and Daniel R. Parisi},
journal= {arXiv preprint arXiv:1907.07702},
year = {2019}
}