3D Lane detection plays an important role in autonomous driving. Recent advances primarily build Birds-Eye-View (BEV) feature from front-view (FV) images to perceive 3D information of Lane more effectively. However, constructing accurate BEV information from FV image is limited due to the lacking of depth information, causing previous works often rely heavily on the assumption of a flat ground plane. Leveraging monocular depth estimation to assist in constructing BEV features is less constrained, but existing methods struggle to effectively integrate the two tasks. To address the above issue, in this paper, an accurate 3D lane detection method based on depth-aware BEV feature transtormation is proposed. In detail, an effective feature extraction module is designed, in which a Depth Net is integrated to obtain the vital depth information for 3D perception, thereby simplifying the complexity of view transformation. Subquently a feature reduce module is proposed to reduce height dimension of FV features and depth features, thereby enables effective fusion of crucial FV features and depth features. Then a fusion module is designed to build BEV feature from prime FV feature and depth information. The proposed method performs comparably with state-of-the-art methods on both synthetic Apollo, realistic OpenLane datasets.

翻译：三维车道线检测在自动驾驶中扮演着重要角色。近期研究主要通过从前视图像构建鸟瞰图特征以更有效地感知车道线的三维信息。然而，由于缺乏深度信息，从前视图像构建精确的鸟瞰图信息存在局限，导致先前工作往往严重依赖平坦地面的假设。利用单目深度估计辅助构建鸟瞰图特征所受约束较少，但现有方法难以有效整合这两项任务。针对上述问题，本文提出一种基于深度感知鸟瞰图特征转换的精确三维车道线检测方法。具体而言，设计了一个有效的特征提取模块，其中集成深度网络以获取三维感知所需的关键深度信息，从而简化视角转换的复杂度。随后提出特征降维模块，用于降低前视特征与深度特征的高度维度，从而实现关键前视特征与深度特征的有效融合。继而设计融合模块，基于原始前视特征与深度信息构建鸟瞰图特征。所提方法在合成数据集Apollo与真实数据集OpenLane上均取得了与最先进方法相当的性能。