Lu Pingping, Xu Shaobing, Peng Huei
IEEE Trans Image Process. 2021;30:2977-2988. doi: 10.1109/TIP.2021.3057287. Epub 2021 Feb 17.
Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm. The former reduces the over-reliance on customized annotated/labeled lane data. We leveraged several open-source semantic segmentation datasets (e.g., Cityscape, Vistas, and Apollo) and designed a dedicated network that can be trained across these heterogeneous datasets to extract lane attributes. The latter algorithm constructs a graph to represent the lane geometry and topology. It does not rely on strong geometric assumptions such as lane lines are a set of parallel polynomials. Instead, it constructs a graph based on detected lane nodes. The lane parameters in the world coordinate are inferred by efficient graph-based searching and calculation. The performance of the proposed method is verified on both open source and our own collected data. On-vehicle experiments were also conducted and the comparison with Mobileye EyeQ2 shows favorable results.
在具有复杂拓扑结构的路段（如车道合并/分流和高速公路匝道）上进行车道检测仍是一个未解决的问题。本文提出了一种新颖的基于图嵌入的解决方案。它由两个关键部分组成，一个基于学习的低级车道特征提取算法和一个基于图嵌入的车道推理算法。前者减少了对定制注释/标记车道数据的过度依赖。我们利用了几个开源语义分割数据集（如Cityscape、Vistas和Apollo），并设计了一个专用网络，该网络可以在这些异构数据集上进行训练以提取车道属性。后者算法构建一个图来表示车道几何形状和拓扑结构。它不依赖于诸如车道线是一组平行多项式之类的强几何假设。相反，它基于检测到的车道节点构建一个图。通过高效的基于图的搜索和计算来推断世界坐标中的车道参数。所提方法的性能在开源数据和我们自己收集的数据上均得到验证。还进行了车载实验，与Mobileye EyeQ2的比较显示出良好的结果。
