Lane detection is essential for many aspects of autonomous driving, such as lane-based navigation and high-definition (HD) map modeling. Although lane detection is challenging especially with complex road conditions, considerable progress has been witnessed in this area in the past several years. In this survey, we review recent visual-based lane detection datasets and methods. For datasets, we categorize them by annotations, provide detailed descriptions for each category, and show comparisons among them. For methods, we focus on methods based on deep learning and organize them in terms of their detection targets. Moreover, we introduce a new dataset with more detailed annotations for HD map modeling, a new direction for lane detection that is applicable to autonomous driving in complex road conditions, a deep neural network LineNet for lane detection, and show its application to HD map modeling.

中文翻译：

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车道检测：具有新结果的调查

车道检测对于自动驾驶的许多方面都是必不可少的，例如基于车道的导航和高清 (HD) 地图建模。尽管车道检测具有挑战性，尤其是在复杂的道路条件下，但在过去几年中，该领域取得了相当大的进展。在本次调查中，我们回顾了最近基于视觉的车道检测数据集和方法。对于数据集，我们通过注释对它们进行分类，为每个类别提供详细描述，并显示它们之间的比较。对于方法，我们专注于基于深度学习的方法，并根据检测目标对其进行组织。此外，我们为高清地图建模引入了一个带有更详细注释的新数据集，这是适用于复杂路况下自动驾驶的车道检测新方向，