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Data-driven Simulation of Pedestrian Collision Avoidance with a Nonparametric Neural Network by Martin, Rafael F.; Parisi, Daniel R. is a scholarly article available to read on EtoBox.

What is Data-driven Simulation of Pedestrian Collision Avoidance with a Nonparametric Neural Network about?

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.

Author

Martin, Rafael F.; Parisi, Daniel R.

Published

2019

Language

EN