  Lane segmentation is a challenging issue in autonomous driving system
designing because lane marks show weak textural consistency due to occlusion or
extreme illumination but strong geometric continuity in traffic images, from
which general convolution neural networks (CNNs) are not capable of learning
semantic objects. To empower conventional CNNs in learning geometric clues of
lanes, we propose a deep network named ContinuityLearner to better learn
geometric prior within lane. Specifically, our proposed CNN-based paradigm
involves a novel Context-encoding image feature learning network to generate
class-dependent image feature maps and a new encoding layer to exploit the
geometric continuity feature representation by fusing both spatial and visual
information of lane together. The ContinuityLearner, performing on the
geometric continuity feature of lanes, is trained to directly predict the lane
in traffic scenarios with integrated and continuous instance semantic. The
experimental results on the CULane dataset and the Tusimple benchmark
demonstrate that our ContinuityLearner has superior performance over other
state-of-the-art techniques in lane segmentation.
