Abstract Lane detection is an application of environmental perception, which aims to detect lane areas or lane lines by camera or lidar. In recent years, gratifying progress has been made in detection accuracy. To the best of our knowledge, this paper is the first attempt to make a comprehensive review of vision-based lane detection methods. First, we introduce the background of lane detection, including traditional lane detection methods and related deep learning methods. Second, we group the existing lane detection methods into two categories: two-step and one-step methods. Around the above summary, we introduce lane detection methods from the following two perspectives: (1) network architectures, including classification and object detection-based methods, end-to-end image-segmentation based methods, and some optimization strategies; (2) related loss functions. For each method, its contributions and weaknesses are introduced. Then, a brief comparison of representative methods is presented. Finally, we conclude this survey with some current challenges, such as expensive computation and the lack of generalization. And we point out some directions to be further explored in the future, that is, semi-supervised learning, meta-learning and neural architecture search, etc.

中文翻译：

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基于深度学习的车道检测方法综述

摘要 车道检测是环境感知的一种应用，旨在通过摄像头或激光雷达检测车道区域或车道线。近年来，在检测精度方面取得了可喜的进展。据我们所知，本文是对基于视觉的车道检测方法进行全面回顾的第一次尝试。首先介绍车道检测的背景，包括传统的车道检测方法和相关的深度学习方法。其次，我们将现有的车道检测方法分为两类：两步法和一步法。围绕上述总结，我们从以下两个角度介绍车道检测方法：（1）网络架构，包括基于分类和目标检测的方法，基于端到端图像分割的方法，以及一些优化策略；(2)相关损失函数。对于每种方法，介绍了它的贡献和弱点。然后，介绍了代表性方法的简要比较。最后，我们用一些当前的挑战来结束这项调查，例如昂贵的计算和缺乏泛化。并且我们指出了一些未来需要进一步探索的方向，即半监督学习、元学习和神经架构搜索等。