Hierarchical Map Difference Translation for Automotive Navigation
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Solution Overview
Problem
Existing automotive navigation map update systems take a significant amount of time to create and distribute difference update data, especially when new roads are constructed or existing roads are modified, leading to delayed information availability for users.
Innovation Solution
A map distribution server with lowermost-level and upper-level difference translation units that produce difference update data by associating link relationships across multiple levels, allowing for efficient creation and distribution of update data, even when only specific road attributes change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If difference update data is created by compiling entire RDB maps and calculating differences between versions, then map data consistency is ensured, but the time required to produce update data becomes excessively long
Solution Approach 1:
The patent segments the map data into multiple hierarchical levels (level 0 to level N), where level 0 represents the finest granularity and higher levels represent aggregated views. This segmentation allows difference calculation to be performed independently at each level, avoiding the need to process entire national maps. The segmenting principle directly resolves the contradiction by enabling faster, localized difference calculation while maintaining consistency through the hierarchical structure that preserves spatial relationships across levels.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional flat map data structure. By organizing map data into multiple levels of aggregation (from detailed level 0 to summarized higher levels), the system adds a temporal and spatial dimension to data processing. This dimensional change allows difference calculation to occur at appropriate granularities, significantly reducing processing time while maintaining data consistency across the hierarchy.
2Reliability
If entire national RDB maps are processed to create difference data, then complete update coverage is achieved, but processing complexity and time increase significantly
Solution Approach 1:
The patent divides the national map data into hierarchical segments (levels 0 to N), where each level represents a different granularity of spatial aggregation. This segmentation reduces processing complexity by allowing difference calculation to be performed on smaller, manageable subsets of data at each level, rather than processing the entire national map at once. The hierarchical structure ensures that update completeness is maintained through the relationships between levels.
Solution Approach 2:
The patent implements partial action by calculating differences only at necessary hierarchical levels based on the scope of changes detected. When minor changes are detected at higher levels, the system can avoid detailed level-by-level processing, performing only the minimum necessary calculations to ensure update completeness. This reduces processing complexity while maintaining reliability.
3Measurement precision
If detailed level-by-level difference calculation is performed, then precise road changes are captured, but processing time becomes unacceptably long
Solution Approach 1:
The patent adds a hierarchical level dimension to the data structure, organizing map information from detailed level 0 to aggregated higher levels. This dimensional change enables a multi-scale processing approach where the system can quickly detect changes at higher levels and then selectively drill down to detailed levels only where changes are detected. This maintains precise road change detection while dramatically improving processing speed by avoiding unnecessary detailed calculations in unchanged areas.
Solution Approach 2:
The patent performs preliminary difference calculation at higher hierarchical levels before conducting detailed level-by-level analysis. By first identifying potential changes at aggregated levels, the system can then focus detailed processing only on specific areas where changes are detected, rather than performing exhaustive detailed calculations across the entire map. This preliminary action maintains detection accuracy while improving overall productivity.
Data Source
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AI summary
Automotive navigation map data has a multilevel structure in which links have a relationship of association with one another among the minutest level and upper levels that are different from the minutest level in scale. A lowermost-level translation unit 13 uses as original data items old and new RDB-form map data items of the minutest level to produce RDB-form difference data of the minutest level. An upper-level difference translation unit 14 produces CSV-format data of a difference of each of the levels of the multilevel structure on the basis of CSV-format data, into which the difference data of the minutest level is translated, and information on the relationship of association among the links of the upper and lower levels in the automotive navigation map data, and integrates the difference data items as final map difference data for an automotive navigation map.