Road Lane Representation Comparison Using Geometric and Semantic Analysis
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Solution Overview
Problem
Navigation and mapping service providers face challenges in comparing and assimilating digital cartographic representations, such as road lane representations, due to inherent errors and uncertainties, which can lead to inaccuracies in digital map data and services.
Innovation Solution
A method and system that compute geometric similarity and process attribute data to determine semantic relationships between cartographic feature representations, generating recommendations for assimilation based on these analyses, using geometric and attribute data in formats like geoJSON, and employing distance metrics like Hausdorff distance for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cartographic representation methods are used, then digital maps can be generated, but inherent errors and uncertainties lead to inaccuracies in comparing and assimilating road lane representations
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between different cartographic representation sources. This system computes geometric similarities using specialized algorithms (like Hausdorff distance) and processes attribute data to determine semantic relationships, thereby reliably determining whether representations correspond to the same or different features despite inherent errors in individual representations
Solution Approach 2:
The patent replaces traditional mechanical/manual methods of cartographic comparison with automated computational systems. These systems use algorithms to compute geometric similarities and process attribute data automatically, eliminating human error and providing consistent, reliable comparisons of cartographic representations
2Adaptability or versatility
If multiple cartographic representation sources are integrated, then more comprehensive digital map data is available, but comparing and assimilating representations from different sources becomes increasingly complex
Solution Approach 1:
The patent segments the complex comparison task into distinct components: geometric similarity computation and attribute data processing. By dividing the assimilation process into these separate analytical stages, the system can handle multiple representation sources systematically without becoming overwhelmed by complexity
Solution Approach 2:
The patent changes the parameters used for comparison by considering both geometric properties (spatial relationships, distances) and attribute properties (semantic meanings, feature characteristics). This multi-parameter approach enables comprehensive integration of diverse cartographic sources while maintaining manageable system complexity through structured analysis
3Productivity
If automated comparison algorithms are used, then processing speed increases, but determining both geometric similarity and semantic relationship requires significant computational resources
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing cartographic representation data before full comparison. This includes preparing geometric data for similarity computation and organizing attribute data for semantic analysis, which reduces the computational burden during actual comparison operations and improves processing efficiency
Data Source
AI summary
An approach is provided for comparing and assimilating road lane representations. The approach, for example, receiving two cartographic feature representations (e.g., digital road lane representations). The approach also involves computing a geometric similarity between the cartographic representations. The approach further involves processing attribute data associated with the cartographic feature representations to determine a semantic relationship between the representations. The approach further involves generating a recommendation with respect to assimilating the representations based on the geometric similarity and the semantic relationship, and providing the recommendation as an output.