  Accurate and reliable lane detection is vital for the safe performance of
lane-keeping assistance and lane departure warning systems. However, under
certain challenging circumstances, it is difficult to get satisfactory
performance in accurately detecting the lanes from one single image as mostly
done in current literature. Since lane markings are continuous lines, the lanes
that are difficult to be accurately detected in the current single image can
potentially be better deduced if information from previous frames is
incorporated. This study proposes a novel hybrid spatial-temporal (ST)
sequence-to-one deep learning architecture. This architecture makes full use of
the ST information in multiple continuous image frames to detect the lane
markings in the very last frame. Specifically, the hybrid model integrates the
following aspects: (a) the single image feature extraction module equipped with
the spatial convolutional neural network; (b) the ST feature integration module
constructed by ST recurrent neural network; (c) the encoder-decoder structure,
which makes this image segmentation problem work in an end-to-end supervised
learning format. Extensive experiments reveal that the proposed model
architecture can effectively handle challenging driving scenes and outperforms
available state-of-the-art methods.
