Robust Lane Detection Framework for Autonomous Vehicles Using Deep Learning and Computer Vision

Abstract

The arrival of self-driving cars has made it even more important to have reliable and effective lane detection systems. Lane detection helps keep cars in their lanes while driving, making roads safer. This paper proposes a framework for real-time lane assistance comprising lane detection, tracking, marking recognition, and a warning when the vehicle is about to leave the lane. The framework uses OpenCV and convolutional neural networks (CNNs) to analyze video from a vehicle-mounted camera and estimate lane parameters such as position, slope, and curvature. These parameters let the framework determine the vehicle's position relative to the lane and steer it. Lane detection systems for self-driving cars must work in real time, adapt to varying weather and lighting conditions, and handle changes in road markings. The proposed framework effectively addresses these challenges and improves the safety and reliability of autonomous driving.