In High-definition (HD) maps, lane elements constitute the majority of components and demand stringent localization requirements to ensure safe vehicle navigation. Vision lane detection with LiDAR position assignment is a prevalent method to acquire initial lanes for HD maps. However, due to incorrect vision detection and coarse camera-LiDAR calibration, initial lanes may deviate from their true positions within an uncertain range. To mitigate the need for manual lane correction, we propose a patch-wise lane correction network (PLCNet) to automatically correct the positions of initial lane points in local LiDAR images that are transformed from point clouds. PLCNet first extracts multi-scale image features and crops patch (ROI) features centered at each initial lane point. By applying ROIAlign, the fix-sized ROI features are flattened into 1D features. Then, a 1D lane attention module is devised to compute instance-level lane features with adaptive weights. Finally, lane correction offsets are inferred by a multi-layer perceptron and used to correct the initial lane positions. Considering practical applications, our automatic method supports merging local corrected lanes into global corrected lanes. Through extensive experiments on a self-built dataset, we demonstrate that PLCNet achieves fast and effective initial lane correction.

翻译：在高清地图中，车道元素占据大部分构件，且需满足严格定位要求以确保车辆安全导航。采用LiDAR位置分配的视觉车道检测是获取高清地图初始车道的常用方法。然而，由于视觉检测误差与相机-激光雷达粗标定，初始车道可能在不确定范围内偏离实际位置。为减少人工车道校正需求，我们提出斑块式车道校正网络（PLCNet），自动校正从点云转换的局部LiDAR图像中初始车道点的位置。PLCNet首先提取多尺度图像特征，并裁剪以各初始车道点为中心的斑块（ROI）特征。通过应用ROIAlign，固定尺寸的ROI特征被展平为一维特征。随后设计一维车道注意力模块，以自适应权重计算实例级车道特征。最终，通过多层感知机推断车道校正偏移量，并用于修正初始车道位置。考虑实际应用，本自动方法支持将局部校正车道合并为全局校正车道。通过在自建数据集上的大量实验，我们证明PLCNet能快速有效实现初始车道校正。