Autonomous vehicles rely heavily on accurate and efficient detection of traffic signs and lane markings to ensure safe navigation. This paper presents a novel semi supervised learning framework to address challenges in traffic sign and lane detection, particularly in scenarios with limited labeled data. By leveraging both labeled and unlabeled datasets, the proposed approach combines supervised learning techniques with self-training and consistency regularization to achieve robust detection and classification