Vehicle steering assist controller design using driver model uncertainty.

Abstract

This dissertation addresses the use of driver model uncertainty for vehicle steering assist controller design, and uses road departure prevention as an application scenario. A robust serial steering assist controller is designed and tested to assess whether lane keeping performance is improved over the cases without a steering assist controller and with a nominal (non-robust) steering assist controller. A system identification approach to compute both the driver model and model uncertainty from driving simulator data is investigated. Both the structured and unstructured uncertainties are computed. The driver model uncertainty is shown to be useful for robust safety systems design. The uncertainty model can represent the uncertainty within one driver over time, or the uncertainty across different drivers, depending on what situation the system is designed for. Based on the computed driver structured uncertainty, a serial robust steering assist controller is designed. The goal of the controller is to avoid the problem associated with the degraded driver performance observed from the identification results. The serial controller is effective in avoiding the low performance situations, and robust stability is achieved. However, due to the large driver model uncertainty involved, robust performance cannot be guaranteed. An adaptive controller is presented as an approach to reduce uncertainty as has been validated in computer simulations. A PC-based driving simulator is used to conduct human-in-the-loop experiments to evaluate the proposed controllers. Two types of experiments are conducted. A long driving task with nominal configurations is designed to evaluate the change in driving behavior over time. A short driving task with artificially large lateral position error is designed so that the driver's regulation performance can be assessed. The experimental results indicate that statistically the robust controller introduces lane-keeping improvements during the short driving experiments. The improvements during the long driving experiments are less evident due to driver adaptation. However, the long driving tests do indicate benefits in terms of ease of driving. A nominal (non-robust) controller that is designed without consideration of driver model uncertainty is also tested. It is found that the nominal controller is characterized by high-gain and should be avoided. The importance of investigating driver uncertainty is therefore justified.