Lane Keeping Control Based on Model Predictive Control Under Region of Interest Prediction Considering Vehicle Motion States

Abstract

To address the failure to consider vehicle states in region of interest (ROI) prediction, we propose the use of a Kalman filter to estimate the position of vehicles relative to lanes by vehicle states on the basis of a vehicle–road micro traffic model in the world coordinate system. The central position of the ROI is determined through a combination of optimal preview time theory with the ROI prediction. The range of the ROI is determined by offsetting upward, downward, leftward, and rightward from the central position of the ROI. The left and right ROI are processed separately to detect lane lines. Simulation results show that the proposed prediction method reduces the ROI range, and the model predictive control controller can make the vehicle run smoothly from the initial position to the road centerline.