Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks

By Qin Zou, Hanwen Jiang, Qiyu Dai, Yuanhao Yue, Long Chen, Qian Wang. IEEE Transactions on Vehicular Technology, 2019

347 citations recorded by the source corpus.

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/13a3b8c5272033ef8d5f2b5c83016995256b2f34

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In Artificial Intelligence

In Automotive Engineering

Xiaodan Liang

In Computer Vision and Pattern Recognition

Citations in the corpus

CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending (2020, methodology): “[36] combines a semantic segmentation CNN and a recurrent neural network to enable a consistent lane detection.”
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection (2020, background): “This section first discusses the dominant approaches, which are based on segmentation [17, 11, 29, 15] or rowwise classification [10, 20, 27], and, subsequently, review solutions in other directions.”