Road Segmentation Using CNN and Distributed LSTM
This work addresses computational efficiency in road segmentation for automated driving systems, though it appears incremental as it builds on existing CNN methods.
The paper tackled road segmentation for automated driving by combining CNN with distributed LSTM layers, resulting in enhanced feature extraction and reduced processing time compared to pure CNN structures.
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.