  Autonomous driving is becoming one of the leading industrial research areas.
Therefore many automobile companies are coming up with semi to fully autonomous
driving solutions. Among these solutions, lane detection is one of the vital
driver-assist features that play a crucial role in the decision-making process
of the autonomous vehicle. A variety of solutions have been proposed to detect
lanes on the road, which ranges from using hand-crafted features to the
state-of-the-art end-to-end trainable deep learning architectures. Most of
these architectures are trained in a traffic constrained environment. In this
paper, we propose a novel solution to multi-lane detection, which outperforms
state of the art methods in terms of both accuracy and speed. To achieve this,
we also offer a dataset with a more intuitive labeling scheme as compared to
other benchmark datasets. Using our approach, we are able to obtain a lane
segmentation accuracy of 99.87% running at 54.53 fps (average).
