Lane detection represents a fundamental task for automated/autonomous vehicles. Current lane detection methods do not provide the versatility of real-time performance, robustness,and accuracy required for real-world scenarios. The reasons include lack of computing power while being portable and inability to observe the continuity and structure of lane lines over a sequence of images. An investigation into the present methods in the literature reveals that deep learning networks cannot focus on relevant images and critical parts of the images. The neural networks implemented with max-pooling operations can cause a loss of information at a granular level of the image during the downsampling of images. Obtaining a fixed set of lane locations will restrict the number of lane lines detected. It hinders the generalisability of the network. This thesis aims to introduce a novel spatio temporal method that can focus on lane lines and key features to increase the robustness and accuracy of lane line detection.The spatio temporal attention network based on Long Short Term Memory (LSTM) units tested on the tvtLane dataset provided an accuracy of 98.1443%, the precision of 0.8873, and F1-score of 0.9108. The precision and F1-measure are the highest when compared to state-of-the-art lane detection networks. The spatio temporal FC attention network produces better accuracy and precision on TuSimple dataset than state-of-the-art networks with 98.2078% and 0.8861, respectively. Testing on the LLAMAS dataset, the network achieved an average precision of 0.8028 and corner recall of 0.7183. The results show the robustness and high accuracy on two different datasets with unique distributions. Although the network is trained on datasets with a maximum of five lanes, it can detect more than five lanes in an image.The network is also able to detect lanes on the unseen Netherlands dataset.