LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating
Deguo Xia, Weiming Zhang, Xiyan Liu, Wei Zhang, Chenting Gong, Xiao, Tan, Jizhou Huang, Mengmeng Yang, Diange Yang

TL;DR
LDMapNet-U introduces an end-to-end system for city-scale lane-level map updating that leverages historical data and deep learning modules to improve accuracy and efficiency, significantly reducing update cycles.
Contribution
The paper presents a novel end-to-end map updating framework that integrates change detection and vectorized map generation, replacing traditional multi-stage manual processes.
Findings
Outperforms traditional methods in accuracy and speed.
Successfully deployed in Baidu Maps for 360+ cities.
Reduces update cycle from quarterly to weekly.
Abstract
An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems. In industrial scenarios, reliance on manual annotation for map updates creates a critical bottleneck. Lane-level updates require precise change information and must ensure consistency with adjacent data while adhering to strict standards. Traditional methods utilize a three-stage approach-construction, change detection, and updating-which often necessitates manual verification due to accuracy limitations. This results in labor-intensive processes and hampers timely updates. To address these challenges, we propose LDMapNet-U, which implements a new end-to-end paradigm for city-scale lane-level map updating. By reconceptualizing the update task as an end-to-end map generation process grounded in historical map…
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