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DevNet: Deviation Aware Network for Lane Detection
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AI
DOI：10.1109/TITS.2022.3170454.png)
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摘要
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Lane detection plays a vital part in autonomous driving. Conventional studies rely on less robust hand-craft features, while deep learning has improved the performance of lane detection to a great extent. Different from dominant methods based on semantic segmentation, this paper proposes an end-to-end framework named DevNet, which combines deviation awareness with semantic features based on point estimation. It consists of two modules to capture more representative features by integrating information of distance deviation and angle which helps to tackle diverse driving conditions in real environments, such as dim or shiny light conditions, crowdedness, and vanishing lanes. Experiments on public datasets indicate that the proposed method achieves favorable performance when compared with the state-of-the-art methods.
Keyword:
Feature extraction
Lane detection
Estimation
Shape
Computer architecture
Semantics
Microprocessors
Autonomous vehicles
lane detection
driving assistance
期刊
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8.4
论文数:
9.9K
被引数:
6.9W
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