Road Network Matching Algorithm Using Crossroad Group Features
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Solution Overview
Problem
Conventional map matching technologies fail to accurately match data between traditional and high-precision maps due to differences in data models, data ranges, and GPS coordinates, leading to difficulties in offset processing and effective data integration for automatic driving applications.
Innovation Solution
A matching algorithm and device that utilize global road network features by extracting crossroad groups, allocating weights based on attribute importance, and evaluating confidence values to match road networks based on relative position relationships, ensuring accurate alignment of road networks between different scale maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS coordinates are used for map matching, then the matching process is simple, but the matching accuracy deteriorates due to coordinate offsets and encryption between different map providers
Solution Approach 1:
The patent introduces road network features as an intermediary element between GPS coordinates and map matching. Instead of directly matching GPS coordinates which have offsets and encryption issues, the system uses road network features (crossroad groups, road segments) as intermediate reference objects that are invariant to coordinate system differences, thereby resolving the contradiction between simple matching process and high matching accuracy
Solution Approach 2:
The patent transforms the matching parameters from GPS coordinates (which have offset and encryption problems) to road network features such as crossroad group configurations, road segment attributes, and topological relationships. This parameter transformation changes the basis of matching from coordinate-based to feature-based, eliminating the accuracy deterioration caused by coordinate system differences
2Adaptability or versatility
If different data models are used for traditional and high-precision maps, then each map can maintain its own characteristics, but the difficulty of data integration increases
Solution Approach 1:
The patent segments the map matching problem into multiple independent components: crossroad group extraction, road segment feature extraction, topological relationship analysis, and confidence value calculation. Each component processes specific aspects of the data independently, allowing different data models to be handled through modular operations rather than requiring complex integrated processing
Solution Approach 2:
The patent creates a universal matching framework that can handle both traditional maps and high-precision maps with different data models. The road network feature extraction and topological relationship analysis methods are designed to be model-agnostic, working with various data formats and structures, thereby reducing integration complexity while preserving map-specific characteristics
3Reliability
If road networks are greatly different due to mapping time differences, then each map reflects current accuracy, but the difficulty of matching increases
Solution Approach 1:
The patent implements a dynamic matching approach that adapts to temporal changes in road networks. The confidence value calculation mechanism dynamically adjusts matching reliability based on the degree of similarity between road network features from different time periods. This allows the system to handle evolving road networks while maintaining matching accuracy, balancing map currency with matching feasibility
Data Source
AI summary
A matching algorithm and device for data with different scales based on global road network features are provided. The method includes: loading data of different maps, constructing crossroad groups in each map, and extracting information on attributes of the crossroad groups; allocating, according to the importance degree of the attribute information, weights for each attribute of the crossroad groups, and comprehensively evaluating confidence values of matching between two crossroad groups in different maps; and constructing, according to the confidence values, matched road networks, calculating total confidence values of the matched road networks, and selecting a road network with the highest total confidence value as an optimal matching solution, namely a matching result.