论文地址
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.233.1475&rep=rep1&type=pdf
道路和车道检测的最新进展调查（Recent Progress in Road and Lane Detection - A survey）
这篇文章写于2014年，是关于道路和车道线感知的一个综述。
Definition 定义
Road and lane understanding includes detecting the extent of the road, the number and position of lanes, merging, splitting and ending lanes and roads, in urban, rural and highway scenarios.
道路和车道的理解包括在城市，乡村和高速公路场景中检测道路的范围，车道的数量和位置，合并、分割和终止车道和道路。
Importance 重要性
The problem of road or lane perception is a crucial enabler for Advanced Driver Assistance Systems
(ADAS).
道路或车道感知问题是高级驾驶员辅助系统（ADAS）的关键推动因素。
Reasons 问题
The main reasons for that are significant gaps in research, high reliability demands and large diversity in case conditions.
其主要原因是研究方面的重大差距，高可靠性要求以及案例条件的多样性。
Relevant modalities 相关方式
- Vision: Vision modality, or more simply put, a camera, is the most frequently used modality for lane and road perception.
视觉：视觉方式，或更简单地说，摄像机，是车道和道路感知最常用的方式。 - LIDAR: Light Detection And Ranging (LIDAR) represents another major possible modality for lane and road detection.
光探测和测距（LIDAR）代表了车道和道路探测的另一种主要可能方式。
Modules and techniques 模块和技术
- Image cleaning: Here, our objective is to remove clutter, misleading imaging artifacts and irrelevant image parts. In general, methods that fall under this module’s scope can be categorized into two families: handling illumination related effects for enhanced image quality, and pruning parts of the image that are suspected as irrelevant for the confronted estimation task.
图像清洁：处理照明相关效果以提高图像质量，剔除图像中与道路及车道无关的部分。 - Feature extraction:Low level features are extracted from the image to support lane and road detection. For road detection, these typically include color and texture statistics allowing road segmentation, road patch classification or curb detection. For lane detection, evidence for lane marks is collected.
特征提取：从图像中提取车道和道路检测所需的特征。 对于道路检测，包括颜色和纹理统计。 对于车道检测，主要是车道标记。 - Road/lane model fitting: A road and lane hypothesis is formed by fitting a road/lane model to the evidence gathered.
道路/车道模型拟合：通过所提取到的特征，对道路/车道模型进行拟合。
Conclusions and Relevance 结论与意义
Challenges 面临的挑战
The challenges for research in the near decade are mainly of two types: extend the scope of road understanding, and increase its reliability. The first challenge is to extend current road and lane detection abilities into new domains. This challenge requires the development of new road scene representations, which are rich enough to describe multiple lanes with non linear topology, and yet can be reliably extracted and tracked from a video stream. The reliability challenge is harder than the first, at least for systems based primarily on vision. The reliability of current systems, which is enough for warning systems, may not be enough for closed-loop features, requiring error rates which are often orders of magnitude lower.
近十年的研究挑战主要有两种：扩大道路理解范围，提高其可靠性。第一个挑战是将当前的道路和车道检测能力扩展到新的领域。 这一挑战需要开发新的道路场景表示，其足够丰富以描述具有非线性拓扑的多个车道，并且可以从视频流中可靠地提取和跟踪。可靠性挑战比第一次更难，至少对于主要基于视觉的系统而言。 对于警报系统而言，当前系统的可靠性对于闭环特征来说可能是不够的，需要通常低几个数量级的错误率。
Fruitful direction 建议方向
- Use modalities other then vision when possible；在可能的情况下使用除视觉之外的其他方式
- Adopt machine learning techniques；采用机器学习技术
- A Public benchmark；A big challenge of current research is the inability to compare performance of
different methods due to the lack of public annotated benchmarks.公共基准，当前研究的一大挑战是由于缺乏公开的基准测试
