Source Map Comparison for Reliable Vehicle Display Integration
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Solution Overview
Problem
The integration of multiple horizontal maps from different sources into a single aircraft display can result in mismatches and display incorrect information due to differences in style, symbology, and layer structures, increasing pilot workload and risk.
Innovation Solution
An automated system and method that compares and integrates horizontal maps by stripping non-relevant data, analyzing common layers, and generating discrepancy maps to identify the best source maps for integration, using airspace-based comparisons and visual object analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple horizontal maps from different sources are integrated into a single display, then pilot workload is reduced and situational awareness is improved, but map mismatches and incorrect information display increase
Solution Approach 1:
The system performs preliminary comparison of source maps before integration by generating plain maps (removing style data), identifying common layers, and creating discrepancy maps to detect mismatches in advance. This preliminary analysis allows the system to identify and alert pilots about potential errors before they affect navigation, thus maintaining reliability while enabling integrated display.
Solution Approach 2:
The system generates discrepancy maps that provide feedback about mismatches between source maps. By comparing common layers and visual objects, the system identifies discrepancies and presents them to pilots through alerts, enabling informed decisions about which source maps to trust and how to interpret the integrated display.
2Productivity
If automated map integration is implemented, then map comparison and integration speed increase, but complexity of the system increases
Solution Approach 1:
The system extracts and removes style data from source maps to create plain maps, isolating only the essential geographical information needed for comparison. This extraction simplifies the comparison process by eliminating distracting visual elements, enabling automated analysis while reducing the computational complexity of comparing stylistic variations.
Solution Approach 2:
The system segments map comparison into distinct operational steps: generating plain maps, identifying common layers, comparing visual objects, and generating discrepancy maps. This segmentation allows complex map comparison to be broken down into manageable tasks that can be automated systematically, improving productivity without overwhelming complexity.
3Measurement precision
If style data is removed from maps for comparison, then map comparison accuracy improves, but map information loss increases
Solution Approach 1:
The system segments map data into style components and geographical information components. By separating these, the system can remove style data for accurate comparison while preserving all geographical information through the plain maps and discrepancy maps. The style information is not lost but isolated, allowing precise comparison without compromising geographical accuracy.
Data Source
AI summary
A method includes receiving map image data of multiple horizontal input maps of an overlapping geographical area and arranged to be displayed on a display device on a vehicle. Each input map has style data and at least one layer associated with a different type of geographical data shown on the map. The method includes generating multiple plain maps comprising removing the style data from the map image data of each input map, and determining common layers between at least two of the plain maps showing the same type of geographical data. The method includes determining data of a discrepancy map of discrepancies between the common layers of the at least two plain maps and for each common layer of the at least two plain maps, and generating an error value of the at least two plain maps comprising using the discrepancy maps.