  Detecting road lanes is challenging due to intricate markings vulnerable to
unfavorable conditions. Lane markings have strong shape priors, but their
visibility is easily compromised. Factors like lighting, weather, vehicles,
pedestrians, and aging colors challenge the detection. A large amount of data
is required to train a lane detection approach that can withstand natural
variations caused by low visibility. This is because there are numerous lane
shapes and natural variations that exist. Our solution, Contrastive Learning
for Lane Detection via cross-similarity (CLLD), is a self-supervised learning
method that tackles this challenge by enhancing lane detection models
resilience to real-world conditions that cause lane low visibility. CLLD is a
novel multitask contrastive learning that trains lane detection approaches to
detect lane markings even in low visible situations by integrating local
feature contrastive learning (CL) with our new proposed operation
cross-similarity. Local feature CL focuses on extracting features for small
image parts, which is necessary to localize lane segments, while
cross-similarity captures global features to detect obscured lane segments
using their surrounding. We enhance cross-similarity by randomly masking parts
of input images for augmentation. Evaluated on benchmark datasets, CLLD
outperforms state-of-the-art contrastive learning, especially in
visibility-impairing conditions like shadows. Compared to supervised learning,
CLLD excels in scenarios like shadows and crowded scenes.
