Sequential Neural Network Model with Spatial-Temporal Attention Mechanism for Robust Lane Detection Using Multi Continuous Image Frames

Poster (2023)
Author(s)

Yongqi Dong (TU Delft - Transport and Planning)

Sandeep Patil (Student TU Delft)

Haneen Farah (TU Delft - Transport and Planning)

H Hellendoorn (TU Delft - Cognitive Robotics)

Research Group
Transport and Planning
Copyright
© 2023 Y. Dong, Sandeep Patil, H. Farah, J. Hellendoorn
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Y. Dong, Sandeep Patil, H. Farah, J. Hellendoorn
Research Group
Transport and Planning
Pages (from-to)
1
Reuse Rights

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Abstract

Lane detection serves as a fundamental task for automated vehicles and Advanced Driver Assistance Systems. However, current lane detection methods can not deliver the versatility of accurate, robust, and realtime compatible lane detection in real-world scenarios especially under challenging driving scenes. Available vision-based methods in the literature do not consider critical regions of the image and their spatial-temporal salience regarding the detection results, thus they deliver poor performance in peculiar difficult circumstances (e.g., serious occlusion, dazzle lighting). This study aims to introduce a novel sequential neural network model with a spatial-temporal attention mechanism that can focus on key features of lane lines and exploit salient spatial-temporal correlations among continuous image frames for the purpose of enhancing the accuracy and robustness of lane detection. Under the regular encoder-decoder structure and with the implementation using common neural network backbones, the proposed model is trained and evaluated on three large-scale opensource datasets. Extensive experiments demonstrate the strength and the robustness of the proposed model outperforming available state-of-the-art methods in various testing.

Files

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