Improvement of Lane Keeping Assistance ADAS Function utilizing a Kalman Filter Prediction of Delayed Position States

Abstract

In designing and implementing control systems, converting simulation based results to real life systems is often not straightforward and may need adaptation of the control approach to achieve similar performance levels to the simulation results. Such adaptations are usually required due to the fact that sensors and actuators have a number of imperfections such as delays, offsets and inherent noise processes. Here, such a problem in relation to the development of a lane keeping control algorithm is presented. An in-house developed lane keeping controller based on a high-fidelity simulation environment was planned to be transferred to a real demonstrator test vehicle. First tests showed significantly deteriorated and unstable performance results of the corresponding controller, which was due to 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 is based on a discrete-time version of the lane tracking model. The approach and the corresponding results were demonstrated using simulation and real vehicle implementation results that were evaluated in real driving conditions.