Deep Learning Based Lane Detection for Auto Driving Vehicles

ABSTRACT—The reliable detection of lane markings is a fundamental and safety-critical perception task for autonomous vehicles and Advanced Driver-Assistance Systems (ADAS). Traditional computer vision methods often fail in challenging real-world scenarios due to variations in lighting, shadows, and road conditions. This paper presents a robust lane detection system based on deep learning. The proposed system frames lane detection as a semantic segmentation problem, employing a Convolutional Neural Network (CNN) with an encoder- decoder architecture, specifically a U-Net, to classify each pixel in a forward-facing camera image as either 'lane' or 'background'. The model is trained on a large-scale public dataset, such as the TuSimple or CULane benchmark, to learn the intricate visual features of lane markings under diverse conditions. The output of the network is a binary segmentation mask from which lane line polynomials are extracted through post-processing. This deep learning-based approach demonstrates superior accuracy and robustness compared to classical methods, providing a reliable foundation for lane-keeping and navigation functionalities in autonomous systems. Keywords—Lane Detection, Deep Learning, Autonomous Driving, ADAS, Computer Vision, Semantic Segmentation, U-Net, Convolutional Neural Network (CNN).