Abstract: Saving lives and reducing road accidents is of great interest while driving at high speeds on freeways. Traffic accidents have a vast majority of fatalities worldwide; hence, lives safety on roads is an important area of interest since decades. The most complicated tasks of future road vehicles are successful lane detection. Lane detection locates lane markers on the road and presents these locations to intelligent system. Intelligent transportation cooperates with infrastructure which provides a safer environment and better traffic conditions. Vision system is one of the basic approach which helps to detect lanes and road boundaries. Currently, camera based systems using computer vision and image processing are used to detect lanes. There are large number of vision based systems which are developed during the last two decades for vehicle control, collision avoidance and lane departure warning. Detecting lane becomes difficult problem because of the varying road conditions and weather conditions that one encounter while driving. This paper describes the implementation of two algorithms with their results using Raspberry Pi. The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer monitor or TV, and uses a standard keyboard and mouse. It is a capable little device that enables people of all ages to explore computing, and to learn how to program in languages like Scratch and Python. It’s capable of doing everything you’d expect a desktop computer to do, from browsing the internet and playing high-definition video, to making spreadsheets, word-processing, and playing games.
Keywords: Lane detection, intelligent vehicle, PLSF, Laplacian filter, Canny Edge detection, Hough transform, Raspberry Pi.