The Fitting of Linear Models to Driver Response Records

An optimal model parameter selection algorithm has been employed in the fitting of linear driver models to a range of experimentally recorded driving data. The driving tasks analyzed were performed in a driving simulator and consisted of lane tracking and obstacle avoidance. It is shown that appreciable differences in driver model performance can result from selecting either an open-loop or a closed-loop analysis framework. The effects of averaging the experimental data before processing are also highlighted with specific examples.