A Novel Strategy for Road Lane Detection and Tracking Based on a Vehicle's Forward Monocular Camera

Modern road vehicles are employing features of driver assistance systems (DAS) to improve drivability performance, comfort, and safety. In the future perspective, the advances in this field will lead these systems to the level of autonomous and cooperative driving, based on sensors networks and sensor fusion. This paper aims to present the readers a novel strategy for lane detection and tracking, which fits as a functional requirement to deploy DAS features like Lane Departure Warning and Lane Keeping Assist. To achieve the presented results, the digital image processing was divided into three levels. At the low-level, the input image dimensionality is reduced from three to one layer, the sharpness is improved, and region of interest is defined based on the minimum safe distance from the vehicle ahead. The feature extractor for lane edges detection design is part of the mid-level processing. The lane tracking strategy development is discussed in the high-level stage; Hough Transform and a shape-preserving spline interpolation are used to achieve a smooth lane fitting. The experimental outcomes were qualitatively and quantitatively evaluated using a ground truth comparison. The strategy shows good accuracy levels, including scenarios with shadows, curves, and road slope.