Improvement of Lane Keeping Assistance ADAS Function utilizing a Kalman Filter Prediction of Delayed Position States 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 inhouse developed lane keeping controller based on a highfidelity 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 apriori predictor. The Kalman filter and apriori predictor design approach is based on a discretetime 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.

A Data Driven Method of Feedforward Compensator Optimization for Autonomous Vehicle Control A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle subsystems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feedforward compensator is designed via a datadriven method to model and optimize the controller performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.