Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird's-eye-view images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird's-eye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving.

中文翻译：

---

车道图估计在城市驾驶中的场景理解

车道级场景注释为自动驾驶车辆提供了宝贵的数据，用于在城市和城市等复杂环境中进行轨迹规划。但是，获得这种数据既费时又昂贵，因为车道注释必须由人为手动注释，因此很难扩展到大面积。在这项工作中，我们提出了一种从鸟瞰图像估计车道几何形状的新颖方法。我们将车道形状和车道连接估计问题公式化为图估计问题，其中车道锚点是图节点，车道线段是图边。我们在从流行的NuScenes数据集及其地图扩展包处理的多模式鸟瞰数据上训练图估计模型。此外，我们使用单独的模型估算每个车道路段的车道连接方向，从而得出有向车道图。我们说明了我们的LaneGraphNet模型在具有挑战性的NuScenes数据集上的性能，并提供了广泛的定性和定量评估。我们的模型在大多数经过评估的城市场景中都显示出令人鼓舞的性能，并且可以作为自动生成高清车道注释以自动驾驶的一步。