Visual Lane Tracking and Curvature Measurement System

This chapter delves into a sophisticated lane detection system designed to bolster vehicle safety through advanced computer vision techniques. The methodology begins with camera calibration to correct distortions, followed by the conversion of images to the HSV color space and the application of thresholding and Sobel operations to create binary images that highlight lane lines. The system then transforms these images into a bird’s-eye view using perspective transformation, making it easier to detect lane curvature and vehicle positioning. A sliding window search algorithm identifies lane pixels, which are then fitted with second-degree polynomials to distinguish left and right lane lines. The curvature of the lane and the vehicle’s deviation from the lane center are computed, providing crucial insights into the vehicle’s positioning. The system demonstrates robust performance with a 96% accuracy in lane detection across diverse road scenarios and illumination conditions. The chapter also explores the potential for future advancements, such as integrating deep learning models and optimizing the algorithm for real-time implementation on embedded systems. This comprehensive approach ensures precise lane detection and enhances road safety by providing timely warnings to drivers, particularly in curved regions prone to overspeeding.