A multi-source vector map fusion method and device

By constructing a global road skeleton and a set of historical vehicle trajectories, and extracting and fusing vector map features, the accuracy and consistency issues of vector maps in complex environments in traditional methods are solved, and high-precision multi-source vector map fusion is achieved.

CN122116058APending Publication Date: 2026-05-29DITU (BEIJING) TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DITU (BEIJING) TECH CO LTD
Filing Date
2024-11-29
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Traditional vector map fusion methods perform poorly in complex environments such as changes in the number of lanes, road divergences, and merging, making it difficult to achieve high accuracy and consistency.

Method used

By acquiring the global road skeleton and historical vehicle trajectory set, point maps and line maps are constructed, vector feature features, feature spatial features and local topological features are extracted, and global topological features and trajectory temporal features are combined for matching and fusion. A variety of graph neural networks and feature fusion algorithms are used to generate a high-precision target vector map.

Benefits of technology

It ensures the topological accuracy and consistency of vector maps, and improves the fusion effect in complex environments, especially the topological derivation capability at intersections and lane bifurcations.

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Abstract

Embodiments of the present application disclose a multi-source vector map fusion method and device. The method of the embodiments of the present application is to obtain a global road skeleton, a historical vehicle trajectory set and original vector maps from multiple data sources, construct a point graph and a line graph of each original vector map respectively, extract vector element features, element spatial features and local topological features from the point graph and the line graph, fuse the vector element features, the element spatial features and the local topological features to obtain a vector feature vector, extract global topological features from the global road skeleton, extract trajectory time sequence features from the historical vehicle trajectory set, match the vector feature vector with the global topological features and the trajectory time sequence features based on a preset feature matching algorithm, and fuse multiple original vector maps with a matching rate higher than a preset matching rate to obtain a target vector map. The method additionally analyzes the global road skeleton and the historical vehicle trajectory set to participate in the fusion of the original vector maps, thereby ensuring topological accuracy and consistency.
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