Comparative study of Lane Detection Algorithms A Review

Abstract: Reducing road accidents and saving lives is of great interest while driving at high speeds on freeways. Traffic accidents account for a vast majority of fatalities worldwide; consequently, improving public and drivers safety on roads has become an important area of interest for many years. Among the complex and challenging tasks of future road vehicles is lane detection. Lane detection is locating lane markers on the road and presenting these locations to intelligent system. Intelligent vehicle cooperate with smart infrastructure to achieve a safer environment and better traffic conditions. To detect lanes and road boundaries, using vision system on the vehicle is one of the principal approaches. Camera based systems relying on computer vision and image processing is one of the most desirable methods used to carry out these functions. There are large numbers of vision based systems for vehicle control, collision avoidance and lane departure warning, which have been developed during the last two decades. Lane detection is a difficult problem because of the varying road and weather conditions that one encounter while driving. This paper shows comparative study of various lane detection algorithms with merits and demerits.

Keywords: Lane detection, intelligent vehicle, Hough transform.