Driver modeling: Data collection, model analysis, and optimization

Abstract: This thesis concerns aspects of driver modeling, with an emphasis on critical near-crash scenarios, involving time spans of around 10 s of driving. Here,driver modeling has been studied using both computer simulations and experiments carried out in a high-fidelity driving simulator. A computer simulation environment has been developed especially for driver modeling. This simulation environment includes a stochastic optimization method for model parameter tuning. Moreover, a review of existing driver models has been carried out. In many cases in the literature, new driver models have been proposed without comparison with existing models. Many models also lack proper validation against driving data. A possible explanation may be that such data are expensive and difficult to collect, especially in critical scenarios. However, in this thesis, the results obtained in a driving simulator study involving a collision avoidance scenario indicate that, at least to some extent, data collected in repeated exposures to a critical event resemble, in many important aspects, data obtained in an unexpected exposure to the same event. Thus, using repeated exposures in a careful manner, one can obtain much larger amounts of available data. In the particular case considered here, the steering behavior was largely conserved between exposures. With increased amounts of data, it becomes possible to carry out formal optimization of driver models (using, for example, the simulation environment presented here) without overfitting model parameters to noise in the data.