Compensation of Sensor and Actuator Imperfections for Lane-Keeping Control Using a Kalman Filter Predictor

This article presents a problem that originates from a control design case study for lateral control of automated automotive vehicles. A lane-keeping control algorithm was developed and tested in a simulation environment and was planned to be implemented in a test vehicle. First, tests showed significantly deteriorated and unstable performance results of the corresponding controller caused by sensor delays and actuator imperfections. After the diagnosis of the problem, an approach to mitigate these issues was undertaken by predicting the delayed sensor data utilizing a linear Kalman filter and an a priori predictor. The Kalman filter and a priori predictor design approach are based on a discrete time version of the lane-tracking model. The proposed measures are easy to be implemented on real-time hardware due to low computational effort. The approach is described using simulation results and verified with results from a test vehicle in real driving conditions.