Attention-Based Two-Stage 3D Lane Detection and Topological Prediction

The increasing demand for accurate perception of static road information in autonomous driving systems has drawn significant attention to 3D lane detection and topology prediction. This paper introduces a two-stage 3D lane detection and topology prediction model based on attention mechanism. The proposed model employs 3D Bézier curves to represent lane lines and an adjacency matrix to present the topological relationships, which facilitate the end-to-end learning of detection and topology prediction for 3D lanes. Experimental results on the OpenLaneV2 dataset demonstrate that the proposed method achieves improvements in terms of both 3D lane detection and topology prediction compared to currently outstanding methods.