LaneDraw: Cascaded lane and its bifurcation detection with nested fusion

Lane and its bifurcation detection is a vital and active research topic in low cost camera-based autonomous driving and advanced driver assistance system (ADAS). The common lane detection pipeline usually predicts lane segmentation mask firstly, and then makes line fitting by parabola or spline post-processing. However, if the speed of the lane and its bifurcation detection is fast and robust enough, we think curve fitting is not a necessary step. The goal of this work is to get accurate lane segmentation, identification of every lane, adaptability of lane numbers and the right combination of lane bifurcation. In this work, we relabeled lane and its bifurcation with solid line if the image of TuSimple dataset has both of them. In the data training process, we apply a data balance strategy for the heavily biased lane and non-lane data. In such a way, we develop a competitive cascaded instance lane detection model and propose a novel bifurcation pixel embedding nested fusion method based on full binary segmentation pixel embedding with self-grouping cluster, called LaneDraw. Our method discards curve fitting process, therefore it reduces the complexity of post-processing and increases detection speed at 35 fps. Moreover, the proposed method yields better performance and high accuracy on the relabeled TuSimple dataset. To the best of our knowledge, this is the first attempt in 2D lane and bifurcation detection, which more often happens in actual situations.

Automated summary

Lane and its bifurcation detection is a crucial topic in low cost camera-based autonomous driving and ADAS. By relabeling dataset and applying data balance strategy, a competitive lane detection model LaneDraw is developed, which improves detection speed and accuracy.