  Safety and decline of road traffic accidents remain important issues of
autonomous driving. Statistics show that unintended lane departure is a leading
cause of worldwide motor vehicle collisions, making lane detection the most
promising and challenge task for self-driving. Today, numerous groups are
combining deep learning techniques with computer vision problems to solve
self-driving problems. In this paper, a Global Convolution Networks (GCN) model
is used to address both classification and localization issues for semantic
segmentation of lane. We are using color-based segmentation is presented and
the usability of the model is evaluated. A residual-based boundary refinement
and Adam optimization is also used to achieve state-of-art performance. As
normal cars could not afford GPUs on the car, and training session for a
particular road could be shared by several cars. We propose a framework to get
it work in real world. We build a real time video transfer system to get video
from the car, get the model trained in edge server (which is equipped with
GPUs), and send the trained model back to the car.
