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

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

Dong, Y. (TU Delft Transport and Planning)
Patil, Sandeep (Student TU Delft)
Farah, H. (TU Delft Transport and Planning)
Hellendoorn, J. (TU Delft Cognitive Robotics)

Cognitive Robotics

2023

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.

http://resolver.tudelft.nl/uuid:01d3bb14-9793-447c-962b-49a70c2b0883

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Transportation Research Board 102nd Annual Meeting 2023, 2023-01-08 → 2023-01-12, Mt Vernon Convention Center, Washington, United States

Institutional Repository

poster

© 2023 Y. Dong, Sandeep Patil, H. Farah, J. Hellendoorn