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.
|Number of pages||1|
|Publication status||Published - 2023|
|Event||The Transportation Research Board (TRB) 102nd Annual Meeting: TRB 102th Annual Meeting - Washington, D.C., Washington, D.C., United States|
Duration: 8 Jan 2023 → 12 Jan 2023
|Conference||The Transportation Research Board (TRB) 102nd Annual Meeting|
|Period||8/01/23 → 12/01/23|