# Road Segmentation Using CNN with GRU

arXiv: https://arxiv.org/abs/1804.05164 · HTML: https://mlsanity.com/paper/1804.05164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Perspective Aware Road Obstacle Detection](https://mlsanity.com/paper/2210.01779/) (2022)
- ["Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks](https://mlsanity.com/paper/2104.01478/) (2021)

## Abstract (arXiv)

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.