Statistical analysis of a steering assist controller using driving simulator data

Human-in-the-loop driving simulator experiments are conducted to evaluate a proposed robust steering assist controller designed based upon driver uncertainty modeling. A nominal controller that is designed without consideration of driver model uncertainty is also tested for comparison. Two types of experiments are proposed. A long driving task with nominal configurations and a short driving task with initially large lateral position error. The data is analyzed using both time domain and frequency domain metrics. In the time domain, the standard deviation of lateral position error and percentage of road departure are used. In the frequency domain, the stability margins and crossover frequency are used. The driving simulator results indicate that statistically the designed robust controller shows improvements in the short driving experiments. The improvements in the long driving experiments are less evident due to driver adaptation. The non-robust nominal controller suffers from high gain and should be avoided. The benefits of considering driver model uncertainty in the design of vehicle steering assist controllers is, therefore, justified.