本节主要讨论的是在自动驾驶应用中，基于前置摄像头拍摄的2D单针图片进行车道线标注的技术，以下是当前车道线标注中常见的内容、标注要求、标注范围和要求、道路条件判断及常见的车道类型识别,以及道路断开的相关信息。
- 全图属性：
- 光照：包含背景光照强度和照射角度,以及摄像机光线对该图像影响的程度。
- 天气：包括当地气候条件（例如温度、湿度、风速等）、是否存在阴雨、暴雪等情况,影响车道线表现的可见性。
- 路况：包括交通流量、交通拥堵程度、行车速度等因素,直接影响车道线是否清晰可见和弯曲程度。
- 目标物类别：明确指出车道线上存在何种类型的标志（如停车标志、应急出口标志、公交车站标志等），如“Curb”、“Parking”、“Bus Stop”。
标注范围和要求：
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条道线定位：
- 单线：单向标注,在图像中沿着车道线中心进行标注。
- 双线：在相邻的两条车道线中，靠近采集车那一侧的车道线进行标注，同一车道线ID保持一致性，例如表示相同的道路参数为“A-B-C-D”。
- 特殊线型：仅适用于主要的双车道线，例如与单线形态类似的直线,宽度较大且较明显的线形。
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消失点判断：
- “消失点”：标记车辆在车道行驶过程中，远离镜头过远，或是在视线范围内经过的一点,通常出现在画面中消失的位置。
- 在画面消失点处，需要标识出聚集成圈的车道线，范围应覆盖图像可见区域,圆心应位于视线内圆形轮廓的直径之内。
- 若是由弯道或隧道阻挡视线形成的消失点,应在实际交通环境下详细判断车辆所在的车道线范围和末端的消失点。
- 清晰表示消失点的位置,不包含视野内空白区域或视线无法覆盖的边缘。
道路条件判断：
- 道路在远端分岔：
- 自动驾驶系统仅需判断当前车辆所在车道的实际状态，而根据实际情况选择对应的车道指示器（如双色线/绿线）进行显示，同时相应地补充相应的表示形式，如绿色线代表左侧行驶、黄色线代表右侧行驶。
- 如果道路上出现了多种分割线，可以通过适当划分出若干条线条来进行跟踪标注，以便于后续操作，如“分叉线-线路”、“并线线-道路”等。
车道类型识别：
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原始单幅图像只标注当前车辆可行驶区域的车道线：
- 新旧车道合并区域进行综合判断，只有前方的车道线才需标明,确保了路径完整性和连贯性。
- 如若相邻的新旧车道之间存在形态差异，可结合最近连续的车道线确定边界,即新旧车道线的交汇点附近。
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道路断开时,需脑补推测具 *** 置：
- 新旧车道线形状和颜色不明显的情况下，可结合周边交通情况，参考周围车道线分布特点，推测新旧车道线断开处可能存在的其它地标性元素，如路边树、建筑物等。
- 标注清晰、准确的位置点,避免出现类似断点而不受交通信号灯或地形约束的情况。
Lane Detection and Characteristics:
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Lane line annotations:
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We mainly focus on the lane line annotation in image capturing applications for autonomous driving, and present the common content, annotation requirements, annotation range and requirements, road conditions judgment, and common lane types identification, as well as lane breakup-related information.
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Lawnline Information:
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全图属性: 包括但不限于环境光照强度和光照角度，以及摄像机光对图像的影响程度，具体的光照特征用于反映道路环境的真实情况,进而影响车道线的表现。
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Weather: 包含地区气候情况，如温度、湿度、风速等，这些因素会影响车道线在画面中的可见性，低亮度、高湿度的天气条件可能导致某些车道线模糊不清,影响驾驶员的判断。
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Road Condition: 包括道路交通流量、交通拥堵程度、行车速度等参数，这些变量直接影响车道线是否清晰可见、是否有弯曲程度，注意交通流的方向、密度和速度等因素,确保所标注车道线能正确反映实时的道路状态。
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Traffic Sign Classifications: 明确标注车道线上存在哪种类型的标志，如“停车标志”、“应急出口标志”、“公交站标志”等,以方便后期实现车道分类的功能。
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Road Closure Conditions Judgement:
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During lane segmentation, only lanes that can be directly driven by the vehicle should be annotated:
- Self-driving systems must only determine the actual lane status based on the situation, and supplement corresponding display forms accordingly when multiple lane segments exist, ensuring the path comprehensiveness and consistency.
- If there are various partitions of lanes within the new and old ones, it is recommended to segment them together with the most recent continuous lane lines, i.e., the intersection point where the new and old lanes merge.
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When lane breaks occur, provide speculative positions:
During situations where the shapes and colors of the lane lines do not clearly distinguish between the new and old lanes, consider the surrounding lane lines' distribution characteristics and rely on the surrounding lane lines for estimation of boundary locations. Inaccurate positioning points can avoid similar disruptions without any traffic signals or environmental constraints.
Lane Types Identification:
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Original single-image image with lane-line annotation:
- Only lanes visible within the current vehicle's driving area need to be annotated. This ensures both path completeness and continuity, avoiding cases where missing lanes lead to incorrect routing.
- If adjacent new and old lanes exhibit different forms, consider considering the latest continuous lane lines as boundaries, i.e., the intersection point where the new and old lanes meet.
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Lane breakup-related issues:
Given that lanes are broken due to specific road conditions (e.g., lines of different colors), we can infer a potential landmark element like street trees, buildings, or other features within the breakage area. Mark these locations accurately, avoiding situations where landmarks are confused by intersections or imprecise bounding boxes.
Improved Emotional Synchronization:
This chapter provides an overview of the commonly encountered aspects of lane line annotation in the context of autonomous driving, covering all the main topics, including content, range and requirements, road conditions determination, lane types identification, and common scenarios. By enhancing the emotional impact and cohesion of the text, the content will convey a sense of understanding and engagement among the target audience. Additionally, emphasizing the importance of precision and consistency across various instances allows readers to grasp the guidelines for accurate lane line annotation, ultimately fostering better collaboration and integration within the broader field of autonomous driving systems.