This article deeply analyzes Baidu Maps' LD-VLG end-to-end foundation model for map generation, aiming to address the slow updates of traditional maps and their inability to adapt to rapidly changing real-world conditions. The article details the evolution of map data production from rule-driven to model-driven, then to multimodal large models, and finally to an end-to-end generative foundation model. It explains how LD-VLG achieves full-process automation in map production through modules such as 3D vision reconstruction, multimodal alignment and fusion, map change chain-of-thought reasoning, and lane-level map update generation. A core advantage of the LD-VLG model lies in its end-to-end architecture, which enables global gradient optimization, effectively eliminating error accumulation between modules. It continuously improves the efficiency and quality of HD map updates through a progressive training strategy, which includes foundation model pre-training, multi-task fine-tuning, reinforcement learning, and a data flywheel. Currently, the model supports lane-level data generation in 360 cities nationwide and covers 13 million kilometers of roads, significantly enhancing Baidu Maps' capabilities in HD navigation and intelligent driving. It also demonstrates real-time update capabilities in practical cases such as lane guidance arrows, lane-level construction, and roadside parking lots.