LDMapNet-U：面向城市级车道级地图更新的端到端系统
LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating
AI总结：
针对传统城市级车道级地图更新依赖人工、流程繁琐、更新不及时的问题，提出端到端系统LDMapNet-U，通过PME和ICP模块同时生成矢量化地图与变化信息，已在百度地图规模化部署，大幅缩短更新周期。
摘要：
最新的城市级车道级地图是保障自动驾驶系统安全性与用户体验的不可或缺的基础设施，也是一项关键支撑技术。在工业场景中，依赖人工标注进行地图更新已成为核心瓶颈。车道级更新需要精准的变化信息，且必须在遵循严格标准的同时，确保与相邻数据的一致性。传统方法采用构建、变化检测、更新三阶段流程，由于精度限制，往往需要人工核验，这导致流程耗费大量人力，也阻碍了更新的及时性。为应对这些挑战，我们提出LDMapNet-U，它实现了城市级车道级地图更新的全新端到端范式。通过将更新任务重构为基于历史地图数据的端到端地图生成过程，我们带来了地图更新的范式转变，可同时生成矢量化地图与变化信息。为实现这一目标，我们引入了先验地图编码（Prior-Map Encoding, PME）模块，可有效编码历史地图，作为检测变化的关键参考。此外，我们还加入了全新的实例变化预测（Instance Change Prediction, ICP）模块，用于学习预测与历史地图的关联。由此，LDMapNet-U可同时完成矢量化地图元素生成与变化检测。为验证LDMapNet-U的优越性与有效性，我们基于大规模真实世界数据集开展了大量实验。此外，LDMapNet-U已于2024年4月在百度地图成功部署上线，支持超过360座城市的地图更新，将更新周期从季度级大幅缩短至周级。更新后的地图服务于数亿用户，并已接入多家头部车企的自动驾驶系统。
英文摘要：
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 data, we introduce a paradigm shift in map updating that simultaneously generates vectorized maps and change information. To achieve this, a Prior-Map Encoding (PME) module is introduced to effectively encode historical maps, serving as a critical reference for detecting changes. Additionally, we incorporate a novel Instance Change Prediction (ICP) module that learns to predict associations with historical maps. Consequently, LDMapNet-U simultaneously achieves vectorized map element generation and change detection. To demonstrate the superiority and effectiveness of LDMapNet-U, extensive experiments are conducted using large-scale real-world datasets. In addition, LDMapNet-U has been successfully deployed in production at Baidu Maps since April 2024, supporting map updating for over 360 cities and significantly shortening the update cycle from quarterly to weekly. The updated maps serve hundreds of millions of users and are integrated into the autonomous driving systems of several leading vehicle companies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
