Lane detection is a crucial task in the field of autonomous driving and advanced driver assistance systems. In recent years, convolutional neural networks (CNNs) have been the primary approach for solving this problem. However, interesting findings from recent research works regarding the use of Transformer models and attention-based mechanisms have shown to be beneficial in the task of semantic segmentation of the road lane markings. In this work, we investigate the effectiveness of incorporating a Vision Transformer (ViT) to process feature maps extracted by a CNN network for lane detection. We compare the performance of a baseline CNN-based lane detection model with that of a hybrid CNN-ViT pipeline and test the model over a well known dataset. Furthermore, we explore the impact of incorporating temporal information from a road scene on a lane detection model’s predictive performance. We propose a post-processing technique that utilizes information from previous frames to improve the accuracy of the lane detection model. Our results show that incorporating temporal information noticeably improves the model’s performance, and manages to make effective corrections over the originally predicted lane masks. Our SegNet backbone, exploiting the proposed post-processing mechanism, reached an F1 scoreof 0.52 and Intersection-over-Union (IoU) of 0.36 over the TuSimple test set. However, the findings from the testing of our CNN-ViT pipeline and a relevant ablation study, do indicate that this hybrid approach might not be a good fit for lane detection. More specifically, the ViT module fails to exploit the feature sextracted by our CNN backbone and therefore, our hybrid pipeline results in less accurate lane marking spredictions.