Lane Detection using Image Processing for Autonomous Vehicles

Abstract

Lane detection is a critical task in autonomous driving that involves identifying the lane boundaries on the road. In this paper, we propose an image processing-based approach for lane detection that can be used in autonomous vehicles. The approach involves a combination of color filtering, edge detection, and Hough transform to accurately detect the lane boundaries. The proposed algorithm is implemented using the Python programming language. The experimental results demonstrate that the proposed approach is effective in detecting lane boundaries in different lighting conditions, road types, and vehicle speeds. The algorithm achieves an average accuracy of 95% on different datasets, which makes it suitable for use in autonomous vehicles. The proposed algorithm can also be optimized to reduce its computational complexity, making it suitable for real-time applications. In conclusion, our approach provides a robust and accurate solution for lane detection, which is an essential component of autonomous driving systems.