Road Course Reconstruction Using Adaptive Geometric Segments
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Solution Overview
Problem
Current methods for reconstructing and displaying road courses on digital maps are inefficient, requiring excessive data storage and complex calculations, especially when achieving high accuracy or detailed representations, and fail to adapt effectively to varying display scales.
Innovation Solution
A method that reconstructs road courses using a combination of polygon connections for lower accuracy and geometric calculations for higher accuracy, with a transition scale at medium detail, limiting the number of support points and interpolation points based on desired accuracy and display resolution, using geometric shapes like straight lines, circular arcs, and clothoids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the course of the road is reconstructed by connecting support points in straight lines (polygon method), then the computational complexity is reduced and data storage requirements are minimized, but the imaging accuracy deteriorates for detailed representations
Solution Approach 1:
The patent applies dynamics by making the reconstruction method adaptable to the display scale. At smaller scales (lower detail requirements), the system uses the simpler polygon method with straight line connections. At larger scales (higher detail requirements), it dynamically switches to the more accurate geometric shape calculation method. This dynamic adaptation resolves the contradiction by allowing the system to use computational simplicity when sufficient and accuracy when needed.
Solution Approach 2:
The patent changes the parameter of reconstruction accuracy based on display scale. By adjusting the level of detail required according to the display scale, the system can accept lower accuracy (polygon method) at small scales where excessive detail would be unnecessary, and demand higher accuracy (geometric method) at large scales where detail is visible and important. This parameter change resolves the contradiction between computational simplicity and imaging accuracy.
2Measurement precision
If the course of the road is reconstructed using geometric shapes (circular arcs, clothoids), then the imaging accuracy is improved for detailed representations, but the computational complexity and data storage requirements increase
Solution Approach 1:
The system dynamically selects between geometric shape calculation and polygon connection based on display scale. Geometric shapes (circular arcs, clothoids) are used only when necessary at larger display scales where high accuracy is visible and important. At smaller scales, the system switches to the simpler polygon method, avoiding unnecessary computational complexity. This dynamic selection resolves the contradiction between accuracy and computational burden.
Solution Approach 2:
The patent applies local quality by using different reconstruction methods in different contexts (local areas of the display). High-accuracy geometric shape reconstruction is applied locally at areas where the display scale requires detailed representation, while the simpler polygon method is used in areas where lower detail is sufficient. This localized application of different quality levels resolves the contradiction between accuracy and computational complexity.
3Measurement precision
If the number of support points is increased to improve curve reconstruction accuracy, then the imaging accuracy is improved, but the data storage requirements significantly increase
Solution Approach 1:
The patent changes the accuracy parameter based on display scale rather than uniformly increasing it. By adjusting the required accuracy level according to the display scale, the system avoids storing excessive support points that would only be necessary for high-accuracy displays. At smaller scales, lower accuracy (fewer support points) is sufficient, reducing data storage requirements while maintaining adequate reconstruction quality.
Solution Approach 2:
The system dynamically adjusts the number of support points needed based on display scale. Rather than storing a fixed large number of support points for all scenarios, the system adapts the support point requirements to the actual display needs. This dynamic adjustment reduces data storage volume by only using the necessary number of support points for each display context.
Data Source
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AI summary
The invention relates to a method for reconstructing a curve, in particular a road, wherein the curve is divided into successive segments (4, 5, 6, 7) in a database (40), in particular a digital road map, to which support points (8, 9, 10, 12, 14, 21, 22, 23) are assigned and for which information about the respective geometric shape of a segment (4, 5, 6, 7) is stored. As long as the desired representation accuracy is less than a predetermined limit accuracy, the curve is reconstructed by connecting at least a subset of the support points (8, 9, 10, 12, 14, 21, 22, 23) in a straight line. Otherwise, the curve profile is calculated depending on the geometric shapes of the segments (4, 5, 6, 7) and the location of the respective associated support points (8, 9, 10, 12, 14, 21, 22, 23).