Abstract: An improved lane line detection and tracking method was proposed for the driving safety of intelligent vehicles under complex illuminations. Gray-level transformation was used for image preprocessing to increase the contrast between lane lines and roads. Lane lines in binary images were extracted by using a progressive probabilistic Hough transform, and were fitted by using a least square method. Dynamic region of interest was established based on Kalman-filter according to the results of the previous image to track the lane lines. The lane line detection and tracking experiments were carried out under different illumination conditions, such as night light, weak light, normal light and strong light. The results show that this method has an accuracy of 97.53% and a real-time performance of 60 ms in the detection and tracking of lane lines, and has robustness against the interference of road shadow, traffic signs, vehicles and other factors.

Key words: intelligent vehicles, driving safety, lane line detection and tracking, complex illumination, gray-level transformation, progressive probabilistic Hough transform, Kalman-filter