论文标题
道路边界提取的卷积复发网络
Convolutional Recurrent Network for Road Boundary Extraction
论文作者
论文摘要
创建包含场景静态元素精确信息的高清晰度地图对于使自动驾驶汽车安全驾驶至关重要。在本文中，我们解决了从激光雷达和摄像机图像中驱动的道路边界提取的问题。为了实现这一目标，我们设计了一个结构化模型，其中一个完全卷积的网络获得了编码道路边界的位置和方向的深度特征，然后，卷积复发网络为每个人的每个卷曲界输出一个多线表示。重要的是，我们的方法是完全自动的，并且不需要循环中的用户。我们在北美大城市上展示了我们方法的有效性，在那里我们以高精度和召回的方式获得了99.3％的公路界限的完美拓扑。
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. Towards this goal, we design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries and then, a convolutional recurrent network outputs a polyline representation for each one of them. Importantly, our method is fully automatic and does not require a user in the loop. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time at a high precision and recall.
