Lane detection with Position Embedding
This work addresses lane detection for autonomous driving systems, but it is incremental as it builds upon an existing method.
The paper tackled lane detection in autonomous driving by enhancing spatial features with position embedding on the RESA model, achieving a top accuracy of 96.93% on the Tusimple dataset.
Recently, lane detection has made great progress in autonomous driving. RESA (REcurrent Feature-Shift Aggregator) is based on image segmentation. It presents a novel module to enrich lane feature after preliminary feature extraction with an ordinary CNN. For Tusimple dataset, there is not too complicated scene and lane has more prominent spatial features. On the basis of RESA, we introduce the method of position embedding to enhance the spatial features. The experimental results show that this method has achieved the best accuracy 96.93% on Tusimple dataset.