Over the years autonomous driving has evolved leaps and bounds and a major part of that was owed to the involvement of deep learning in computer vision. Even in modern autonomous driving, multi-lane detection and projection has been a challenge which needs to be solved further. Several approaches have been proposed earlier involving conventional threshold techniques along with graphical models or with RANSAC and polynomial fitting. In recent times direct regression using deep learning models is also explored. In this paper, we propose a blend which uses a deep learning model for initial lane detection at pixel level and conditional random fields for modeling of lanes. The method provides a 15% improvement in lane detection and projection over conventional models.