Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution. In this paper, we present a new method for lane detection based on relay chain prediction. Specifically, our model predicts a segmentation map to classify the foreground and background region. For each pixel point in the foreground region, we go through the forward branch and backward branch to recover the whole lane. Each branch decodes a transfer map and a distance map to produce the direction moving to the next point, and how many steps to progressively predict a relay station (next point). As such, our model is able to capture the keypoints along the lanes. Despite its simplicity, our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.

翻译：通道探测是许多现实世界自主系统的重要组成部分。 尽管提出了各种各样的通道探测方法, 并报告了长期稳步的基准改进, 但通道探测在很大程度上仍然是一个尚未解决的问题。 这是因为大部分现有的通道探测方法要么将通道探测视为密集预测, 要么将通道探测视为密集预测, 要么将探测任务视为探测任务, 很少有人考虑车道标记的独特地形( Y-shape, Fork-shape, 近乎横向通道), 从而导致次优化的解决方案。 在本文中, 我们根据中继链预测提出了一个新的航道探测方法。 具体地说, 我们的模型预测了对地表和背景区域进行分类的分割图。 对于前方区域的每个像素点, 我们通过前方分支和后方分支来恢复整个航道。 每个分支都破译了传输地图和距离图, 以绘制向下一点的方向, 以及逐步预测中继站( 下点) 。 因此, 我们的模型可以捕捉到沿车道的关键点。 尽管它的简单性, 我们的战略允许我们建立新的州- 、 TuS-LA 四号主要基准, 。