  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.
