Detection of Lane departure assumes a critical role in bolstering the safety features of Advanced Driver Assistive Systems, profoundly influencing active and secure driving experiences. This project introduces an extensive lane detection methodology grounded in computer vision techniques, leveraging video streams captured by a roof-mounted camera atop a vehicle. The intricate process involves meticulous correction of camera distortion, the strategic application of HLS and Sobel operation with threshold to accentuate lane lines in a binary image, and subsequent perspective transforms that morph the resultant frame into a bird’s-eye view. Identification of the lane lines are performed based on a sliding window search and next discerned through the fitting of second-degree polynomials. The lane identification process includes computations regarding lane centre deviation and lane curve. The identified lane boundaries are re-projected onto the input image to facilitate the calculation of lane curve and vehicle position. Python programming language used for implementing this technique and OpenCV for image processing. Across a spectrum of challenging lighting conditions it performs consistently and accurately in detecting lane lines.