Digital Map Discrepancy Detection via Travel Direction Analysis
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Solution Overview
Problem
Maintaining accurate digital map data is challenging due to continuous changes in real-world features like road networks, making it difficult to keep map data up-to-date manually, especially for large areas like the Netherlands or the United States.
Innovation Solution
A method is provided to identify discrepancies in digital map data by selecting candidate locations from positional data, allocating them to categories based on travel direction distributions, and comparing these against a database of map data to detect potential discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual maintenance of map data is attempted for large areas, then map data accuracy can be maintained, but the task becomes extremely difficult or near impossible
Solution Approach 1:
The system enables map data to be automatically updated and maintained through self-service mechanisms. Positional data from mobile devices is automatically processed to identify and correct discrepancies in map data without requiring manual intervention for every change, making the system self-updating and reducing operational difficulty
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of manual review and updating of map data, the system uses automated analysis of positional data from multiple devices to detect and identify discrepancies, substituting human effort with computational algorithms
2Ease of operation
If automated detection methods are used to maintain map data, then operational difficulty is reduced, but the complexity of the detection system increases
Solution Approach 1:
The system uses universal methods that can handle multiple types of map features and discrepancies through a single automated framework. The discrepancy identification apparatus applies the same analytical processes to various geographic features (roads, buildings, landmarks) regardless of their specific type, reducing the need for separate specialized systems for each feature category
Solution Approach 2:
The system introduces an intermediary automated analysis layer between raw positional data and map data maintenance. This intermediary apparatus processes positional data from multiple devices, identifies discrepancies through analytical methods, and presents findings for verification, simplifying the overall process while managing system complexity through modular design
3Measurement precision
If positional data from multiple devices is analyzed to detect discrepancies, then map data accuracy improves, but the amount of data to be processed increases
Solution Approach 1:
The system segments the large volume of positional data into manageable analytical units. By processing data from multiple devices in a structured manner and analyzing positional information in organized sequences, the system can handle large data volumes efficiently while maintaining high accuracy in discrepancy detection
Solution Approach 2:
The system merges positional data from multiple independent devices to collectively identify discrepancies. By combining observations from numerous devices tracking the same geographic features, the system achieves higher detection accuracy through aggregated data while processing the combined information efficiently through coordinated analysis
Data Source
AI summary
Embodiments of the present invention provide a method for identifying discrepancies in digital map data, comprising selecting one or more candidate locations as a subset of locations within positional data relating to the movement of a plurality of devices with respect to time in an area, allocating each of the candidate locations to one or more predetermined categories based upon a distribution of travel directions of the devices at each candidate location and comparing the candidate locations against a database of map data and identifying locations of possible discrepancies in the digital map data based upon the category of each candidate location.