  Lane detection in driving scenes is an important module for autonomous
vehicles and advanced driver assistance systems. In recent years, many
sophisticated lane detection methods have been proposed. However, most methods
focus on detecting the lane from one single image, and often lead to
unsatisfactory performance in handling some extremely-bad situations such as
heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In
fact, lanes are continuous line structures on the road. Consequently, the lane
that cannot be accurately detected in one current frame may potentially be
inferred out by incorporating information of previous frames. To this end, we
investigate lane detection by using multiple frames of a continuous driving
scene, and propose a hybrid deep architecture by combining the convolutional
neural network (CNN) and the recurrent neural network (RNN). Specifically,
information of each frame is abstracted by a CNN block, and the CNN features of
multiple continuous frames, holding the property of time-series, are then fed
into the RNN block for feature learning and lane prediction. Extensive
experiments on two large-scale datasets demonstrate that, the proposed method
outperforms the competing methods in lane detection, especially in handling
difficult situations.
