Enhancing safety and minimizing road accidents to save lives is a significant focus within the realm of Advanced Driver Assistance Systems (ADAS). Road lane detection, or the identification of road boundaries, stands out as a complex and demanding task for future road vehicles. This procedure entails localizing the road, figuring out how far the car is from the road, and assessing the direction of the car's heading. A key strategy for detecting road boundaries and lanes involves utilizing a vision system within the vehicle. However, lane detection poses challenges due to the diverse road conditions encountered while driving. This work presents a vision-based lane detection method that can operate in real-time and is resilient to variations in light and shadow. The system captures the front view through a vehicle-mounted camera and employs various processes to detect the lanes. By fitting a pair of hyperbolas to the lane edges and utilizing the Hough transform, the lanes are extracted. Notably, this proposed lane detection system applies to both painted and unpainted roads, accommodating curved and straight road segments across various weather conditions. Experimental testing of this approach reveals robust and efficient performance, meeting the real-time requirements. The results highlight the system's capability to handle different road types and weather scenarios. Lastly, a critical evaluation of the methods is provided, discussing their prospective use in the future.