  Lane detection is crucial for vehicle localization which makes it the
foundation for automated driving and many intelligent and advanced driving
assistant systems. Available vision-based lane detection methods do not make
full use of the valuable features and aggregate contextual information,
especially the interrelationships between lane lines and other regions of the
images in continuous frames. To fill this research gap and upgrade lane
detection performance, this paper proposes a pipeline consisting of self
pre-training with masked sequential autoencoders and fine-tuning with
customized PolyLoss for the end-to-end neural network models using
multi-continuous image frames. The masked sequential autoencoders are adopted
to pre-train the neural network models with reconstructing the missing pixels
from a random masked image as the objective. Then, in the fine-tuning
segmentation phase where lane detection segmentation is performed, the
continuous image frames are served as the inputs, and the pre-trained model
weights are transferred and further updated using the backpropagation mechanism
with customized PolyLoss calculating the weighted errors between the output
lane detection results and the labeled ground truth. Extensive experiment
results demonstrate that, with the proposed pipeline, the lane detection model
performance on both normal and challenging scenes can be advanced beyond the
state-of-the-art, delivering the best testing accuracy (98.38%), precision
(0.937), and F1-measure (0.924) on the normal scene testing set, together with
the best overall accuracy (98.36%) and precision (0.844) in the challenging
scene test set, while the training time can be substantially shortened.
