Road Segmentation Using CNN with GRU
This work addresses the need for fast and efficient road segmentation in autonomous driving, though it is incremental as it combines existing CNN and GRU components for a specific application.
The paper tackles road segmentation for autonomous vehicles by proposing a CNN-GRU network that reduces computational complexity compared to deep encoder-decoder methods, achieving high accuracy on the KITTI benchmark with real-time processing speed.
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.