  In automated driving systems (ADS) and advanced driver-assistance systems
(ADAS), an efficient road segmentation is necessary to perceive the drivable
region and build an occupancy map for path planning. The existing algorithms
implement gigantic convolutional neural networks (CNNs) that are
computationally expensive and time consuming. In this paper, we introduced
distributed LSTM, a neural network widely used in audio and video processing,
to process rows and columns in images and feature maps. We then propose a new
network combining the convolutional and distributed LSTM layers to solve the
road segmentation problem. In the end, the network is trained and tested in
KITTI road benchmark. The result shows that the combined structure enhances the
feature extraction and processing but takes less processing time than pure CNN
structure.
