Drift and diffusion based models of driver behavior

Based on experimental data taken from a driving simulator, we present different ways of how a stochastic model of the driver’s behavior can be estimated directly from the data. The aim is to generate a drift and diffusion based stochastic model, which is equivalent to a potential based model. Depending on the data, different strategies are presented. Even in the case of non-equidistant or transient data, the method will be used to estimate appropriate models in one or higher dimensions.