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Stochastic modeling of driver behavior by Langevin equations

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Abstract

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.

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Authors and Affiliations

  1. OFFIS, Escherweg 2, 26121, Oldenburg, Germany

    Michael Langner

  2. Institute of Physics, Carl-von-Ossietzky University, 26111, Oldenburg, Germany

    Joachim Peinke

Authors
  1. Michael Langner
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  2. Joachim Peinke
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Correspondence to Michael Langner.

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Langner, M., Peinke, J. Stochastic modeling of driver behavior by Langevin equations. Eur. Phys. J. B 88, 137 (2015). https://doi.org/10.1140/epjb/e2015-60239-6

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  • DOI: https://doi.org/10.1140/epjb/e2015-60239-6

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