A Robust Road Lane Detection Using Computer Vision Approach for Autonomous Vehicles

The quality of human life is improving over a period of time with the technological advancements in the arena of Artificial Intelligence and Internet of Things. Various fields adopt and implement automation process for eco-nomic growth. The recent advancement and transition to automated vehicles is one of the key paradigms in Intelligent Systems. In order to improve the safety and road accidents, detection of road lanes is required to be done with more accuracy. Utilizing computer vision is a potential method to aid drivers in challenging situations, enhancing both their safety and comfort. The essential components of road lane detection include Line parameter estimation, selection of the Region of Interest (ROI) and astute edge detection. This research work aims to use computer vision techniques for the detection of road lane lines. It utilizes image processing algorithms to identify and track the road lane markings in real-time. The proposed system makes use of edge detection techniques and Hough transform to detect straight lines on the road. It then combines this information with other image processing techniques to accurately determine the lane boundaries. The algorithm is able to accurately detect and classify road lane lines in real-time, achieving a high detection accuracy of 97.5%. The performance achieved demonstrate the efficiency of the proposed method in enabling autonomous vehicles to navigate safely and stay within designated boundaries.