Lane Detection and Tracking Using Morphology and Multiple ROI

Abstract

Lane detection algorithm have been used for passenger safety systems of luxury vehicle such as the lane keeping assist system (LKAS) and the lane departure warning system (LDWS). In order to enhance the performance of passenger safety systems, robustness and low computation load is required for an effective lane detection algorithm. In previous studies, edge detection, pattern recognition, and probabilistic method have been applied for lane detection. However these approaches have some limitations such as high sensitivity to noise, non-uniform illumination, and high computation load. In this study, we proposed a robust lane detection algorithm using morphology and tracking methods. Morphology is used as a preprocessor for lane detection to reduce the influence of cracked road surfaces and shadows. Moreover, we applied tracking method using multiple regions of interest (ROI) windows to reduce the computation time. In particular, the sizes of multiple ROI were determined by considering the geometric scale effect of the lane mark width. The proposed lane detection and tracking algorithm was evaluated on various road conditions and large scale data. As a result, the proposed algorithm proved to be robust and fast enough to apply to real-time safety-critical systems