  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.
