Accurate lane detection is critical for navigation in autonomous vehicles,
particularly the active lane which demarcates the single road space that the
vehicle is currently traveling on. Recent state-of-the-art lane detection
algorithms utilize convolutional neural networks (CNNs) to train deep learning
models on popular benchmarks such as TuSimple and CULane. While each of these
models works particularly well on train and test inputs obtained from the same
dataset, the performance drops significantly on unseen datasets of different
environments. In this paper, we present a real-time robust neural network
output enhancement for active lane detection (RONELD) method to identify,
track, and optimize active lanes from deep learning probability map outputs. We
first adaptively extract lane points from the probability map outputs, followed
by detecting curved and straight lanes before using weighted least squares
linear regression on straight lanes to fix broken lane edges resulting from
fragmentation of edge maps in real images. Lastly, we hypothesize true active
lanes through tracking preceding frames. Experimental results demonstrate an up
to two-fold increase in accuracy using RONELD on cross-dataset validation
tests.

本文介绍了一种实时的、鲁棒的神经网络输出增强方法 (RONELD)，用于从深度学习的概率地图输出中识别、跟踪和优化行驶中的车道，并在跨数据集验证测试中展示出多达两倍的精度提升。

RONELD: 主动车道检测的鲁棒神经网络输出增强

RONELD: Robust Neural Network Output Enhancement for Active Lane  Detection

Accurate lane detection, a crucial enabler for autonomous driving, currently
relies on obtaining a large and diverse labeled training dataset. In this work,
we explore learning from abundant, randomly generated synthetic data, together
with unlabeled or partially labeled target domain data, instead. Randomly
generated synthetic data has the advantage of controlled variability in the
lane geometry and lighting, but it is limited in terms of photo-realism. This
poses the challenge of adapting models learned on the unrealistic synthetic
domain to real images. To this end we develop a novel autoencoder-based
approach that uses synthetic labels unaligned with particular images for
adapting to target domain data. In addition, we explore existing domain
adaptation approaches, such as image translation and self-supervision, and
adjust them to the lane detection task. We test all approaches in the
unsupervised domain adaptation setting in which no target domain labels are
available and in the semi-supervised setting in which a small portion of the
target images are labeled. In extensive experiments using three different
datasets, we demonstrate the possibility to save costly target domain labeling
efforts. For example, using our proposed autoencoder approach on the llamas and
tuSimple lane datasets, we can almost recover the fully supervised accuracy
with only 10% of the labeled data. In addition, our autoencoder approach
outperforms all other methods in the semi-supervised domain adaptation
scenario.

本文提出了一种基于自动生成的合成数据和未标记或部分标记的目标域数据学习的自动驾驶准确车道检测方法，采用了基于自编码器的方法处理合成和真实数据，同时探索了领域自适应方法和半监督学习，经过实验验证，该方法几乎能够在仅有 10％标注数据的情况下实现全监督的准确率。