Efficient Sequential Neural Network with Spatial-Temporal Attention and Linear LSTM for Robust Lane Detection Using Multi-Frame Images

• URL: http://arxiv.org/abs/2602.03669v1
• Date: Tue, 03 Feb 2026 15:51:29 GMT
• Title: Efficient Sequential Neural Network with Spatial-Temporal Attention and Linear LSTM for Robust Lane Detection Using Multi-Frame Images
• Authors: Sandeep Patil, Yongqi Dong, Haneen Farah, Hans Hellendoorn,
• Abstract summary: Lane detection is a crucial perception task for automated vehicles (AVs) and Advanced Driver Assistance Systems.<n>Current methods lack versatility in delivering accurate, robust, and real-time compatible lane detection.<n>This study introduces a novel sequential neural network model with a spatial-temporal attention mechanism to focus on key features of lane lines.
• Score: 3.8825198843426345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lane detection is a crucial perception task for all levels of automated vehicles (AVs) and Advanced Driver Assistance Systems, particularly in mixed-traffic environments where AVs must interact with human-driven vehicles (HDVs) and challenging traffic scenarios. Current methods lack versatility in delivering accurate, robust, and real-time compatible lane detection, especially vision-based methods often neglect critical regions of the image and their spatial-temporal (ST) salience, leading to poor performance in difficult circumstances such as serious occlusion and dazzle lighting. This study introduces a novel sequential neural network model with a spatial-temporal attention mechanism to focus on key features of lane lines and exploit salient ST correlations among continuous image frames. The proposed model, built on a standard encoder-decoder structure and common neural network backbones, is trained and evaluated on three large-scale open-source datasets. Extensive experiments demonstrate the strength and robustness of the proposed model, outperforming state-of-the-art methods in various testing scenarios. Furthermore, with the ST attention mechanism, the developed sequential neural network models exhibit fewer parameters and reduced Multiply-Accumulate Operations (MACs) compared to baseline sequential models, highlighting their computational efficiency. Relevant data, code, and models are released at https://doi.org/10.4121/4619cab6-ae4a-40d5-af77-582a77f3d821.

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