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.

本文提出一种基于中继链路预测的车道检测新方法，通过预测前向和反向分支的传输图和距离图，以逐步预测中继站（下一个点）的方向和步数，从而能够捕获车道上的关键点。该方法在 TuSimple，CULane，CurveLanes 和 LLAMAS 这四个主要基准测试中取得了新的最佳效果。