https://doi.org/10.1140/epjb/e2015-60239-6
Regular Article
Stochastic modeling of driver behavior by Langevin equations
1 OFFIS, Escherweg 2, 26121 Oldenburg, Germany
2 Institute of Physics, Carl-von-Ossietzky University, 26111 Oldenburg, Germany
a
e-mail: michael.langner@mail.uni-oldenburg.de
Received: 25 March 2015
Received in final form: 1 April 2015
Published online: 1 June 2015
A procedure based on stochastic Langevin equations is presented and shows how a stochastic model of driver behavior can be estimated directly from given data. The Langevin analysis allows the separation of a given data-set into a stochastic diffusion- and a deterministic drift field. Form the drift field a potential can be derived. In particular the method is here applied on driving data from a simulator. We overcome typical problems like varying sampling rates, low noise levels, low data amounts, inefficient coordinate systems, and non-stationary situations. From the estimation of the drift- and diffusion vector-fields derived from the data, we show different ways how to set up Monte-Carlo simulations for the driver behavior.
Key words: Statistical and Nonlinear Physics
© EDP Sciences, Società Italiana di Fisica, Springer-Verlag, 2015
