I. Introduction
In recent years, autonomous driving has received much attention in computer vision and robotics research, at both academic and industrial levels. A critical step in autonomous driving is the recognition of the operating environment by the vehicle. Road lane markings form an integral component of this operating environment. In particular, active lane markings serve as significant cues for constraining the maneuver of vehicles on roads by indicating the active lane, which is the single usable road space by the vehicle, that serves as input for lateral steering control to avoid collisions with other road users. Despite the pressing need for accurate and reliable lane detection to enable successful autonomous vehicles, detecting lanes has remained challenging throughout the years. One reason is the rather simple and homogeneous appearance of lane markings which lacks distinctive features. Other obstacles, such as weather and illumination conditions, also plague lane detection research. Furthermore, lane detection scenarios occur in diverse driving environments, various road surface conditions, and in real-time, which necessitates a robust and low computational cost algorithm for successful lane detection on autonomous vehicles.