Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by the lack of comprehensive LiDAR datasets. The sparse nature of LiDAR point cloud data prevents an efficient manual annotation process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane dataset that comprises 20k frames of surround-view LiDAR point clouds with enriched semantic annotation. Unlike existing datasets confined to a frontal perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the ego vehicle, capturing complex lane patterns in both urban and highway environments. We leverage the geometric traits of lane lines and the intrinsic spatial attributes of LiDAR data to design a simple yet effective automatic annotation pipeline for generating finer lane labels. To propel future research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet, incorporating the spatial geometry learning of the LiDAR point cloud into Bird's Eye View (BEV) based lane identification. Experimental results indicate that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D lane detection task on the K-Lane dataset and our LiSV-3DLane.

翻译：高级驾驶辅助系统（ADAS）已成功将基于学习的技术整合到车辆感知与决策中。然而，这些技术在有效驾驶环境感知的3D车道检测应用中，因缺乏综合性的LiDAR数据集而受到制约。LiDAR点云数据的稀疏特性阻碍了高效的人工标注流程。为解决此问题，我们提出了LiSV-3DLane——一个大规模3D车道数据集，包含2万帧环视LiDAR点云及其丰富的语义标注。与仅限于前视角的现有数据集不同，LiSV-3DLane提供了自车周围完整的360度空间全景，捕捉了城市和高速公路环境中复杂的车道模式。我们利用车道线的几何特征及LiDAR数据的内在空间属性，设计了一种简单而有效的自动标注流水线，用于生成更精细的车道标签。为推动未来研究，我们提出了一种新颖的基于LiDAR的3D车道检测模型LiLaDet，将LiDAR点云的空间几何学习融入基于鸟瞰图（BEV）的车道识别中。实验结果表明，在K-Lane数据集及我们提出的LiSV-3DLane上，LiLaDet在3D车道检测任务中优于现有的基于相机和基于LiDAR的方法。