Lane detection is typically tackled with a two-step pipeline in which a
segmentation mask of the lane markings is predicted first, and a lane line
model (like a parabola or spline) is fitted to the post-processed mask next.
The problem with such a two-step approach is that the parameters of the network
are not optimized for the true task of interest (estimating the lane curvature
parameters) but for a proxy task (segmenting the lane markings), resulting in
sub-optimal performance. In this work, we propose a method to train a lane
detector in an end-to-end manner, directly regressing the lane parameters. The
architecture consists of two components: a deep network that predicts a
segmentation-like weight map for each lane line, and a differentiable
least-squares fitting module that returns for each map the parameters of the
best-fitting curve in the weighted least-squares sense. These parameters can
subsequently be supervised with a loss function of choice. Our method relies on
the observation that it is possible to backpropagate through a least-squares
fitting procedure. This leads to an end-to-end method where the features are
optimized for the true task of interest: the network implicitly learns to
generate features that prevent instabilities during the model fitting step, as
opposed to two-step pipelines that need to handle outliers with heuristics.
Additionally, the system is not just a black box but offers a degree of
interpretability because the intermediately generated segmentation-like weight
maps can be inspected and visualized. Code and a video is available at
github.com/wvangansbeke/LaneDetection_End2End.

本文提出了一种利用端到端的方式，通过直接回归车道线参数来训练车道检测器，利用堆叠的深度网络生成的每个车道线的分割权重图与可微的最小二乘拟合模块来进行训练，并且通过观察到拟合算法可以反向传播，实现了端到端的训练方法，从而避免了引入流程中的不确定性。