LAUSR

Lane detection is a fundamental task in intelligent driving systems. However, the slender and sparse structure of lanes, combined with the dominance of irrelevant background regions in road scenes, makes accurate lane localization particularly challenging, especially under complex and adverse conditions. To address these issues, we propose a novel Region‐Aware Sparse Attention Network (RSANet), which is designed to selectively enhance lane‐relevant features while suppressing background interference. Specifically, we introduce the Region‐guided Pooling Predictor (RPP) that generates lane region activation maps to guide the backbone network in focusing on informative areas. To improve the multi‐scale feature fusion capability of the Feature Pyramid Network (FPN), we propose the Bilateral Pooling Attention Module (BPAM) that captures discriminative features by jointly modeling dependencies along both the channel and spatial dimensions. Furthermore, the Lane‐guided Sparse Attention Mechanism (LSAM) efficiently aggregates global contextual information from the most relevant spatial regions to reinforce lane prior representations while significantly reducing redundant computation. Extensive experiments on benchmark datasets demonstrate that RSANet outperforms state‐of‐the‐art methods in a variety of challenging scenarios. Notably, RSANet achieves an F1@50 score of 80.04% on the CULane dataset that shows notable improvements.