  This paper presents an accurate and fast algorithm for road segmentation
using convolutional neural network (CNN) and gated recurrent units (GRU). For
autonomous vehicles, road segmentation is a fundamental task that can provide
the drivable area for path planning. The existing deep neural network based
segmentation algorithms usually take a very deep encoder-decoder structure to
fuse pixels, which requires heavy computations, large memory and long
processing time. Hereby, a CNN-GRU network model is proposed and trained to
perform road segmentation using data captured by the front camera of a vehicle.
GRU network obtains a long spatial sequence with lower computational
complexity, comparing to traditional encoder-decoder architecture. The proposed
road detector is evaluated on the KITTI road benchmark and achieves high
accuracy for road segmentation at real-time processing speed.
