Geographical Zone Partitioning for Fast Vehicle Point-in-Polygon Search
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Solution Overview
Problem
Determining whether a vehicle is located within a complex geographical zone with many vertices is computationally difficult and resource-intensive, especially when managing large numbers of vehicles, as existing point-in-polygon algorithms require significant computing resources.
Innovation Solution
The method involves defining a bounding box around the geographical zone, iteratively partitioning it into smaller bounding boxes to reduce vertex count, generating a binary tree data structure, and using this structure to efficiently determine vehicle location within the zone through a point-in-polygon test.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point-in-polygon algorithms are used to determine vehicle location within complex geographical zones, then measurement precision is maintained, but computing resources and processing time increase significantly
Solution Approach 1:
The patent divides a complex geographical zone with many vertices into multiple smaller sub-zones, each with fewer vertices. This segmentation reduces the computational complexity of point-in-polygon tests by breaking down the original complex polygon into simpler constituent polygons, thereby reducing the computing resources required while maintaining location determination accuracy.
2Measurement precision
If point-in-polygon algorithms are used to determine vehicle location within complex geographical zones, then measurement precision is maintained, but processing time increases significantly
Solution Approach 1:
The patent divides a complex geographical zone with many vertices into multiple smaller sub-zones, each with fewer vertices. This segmentation reduces the computational complexity of point-in-polygon tests by breaking down the original complex polygon into simpler constituent polygons, thereby reducing the processing time required while maintaining location determination accuracy.
3Measurement precision
If complex geographical zones with many vertices are used to accurately represent areas of interest, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides a complex geographical zone with many vertices into multiple smaller sub-zones, each with fewer vertices. This segmentation reduces the device complexity by breaking down the complex polygon into simpler constituent polygons, making the data structures easier to manage and process while maintaining the overall geographical representation accuracy.
Solution Approach 2:
The patent creates a hierarchical structure where the complex geographical zone is divided into parent sub-zones, which can further be divided into child sub-zones. This nested structure organizes the complex data in a manageable hierarchy, reducing device complexity by allowing efficient traversal from general to specific regions rather than dealing with one monolithic complex polygon.
Data Source
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AI summary
Systems and methods for processing geographical zones are provided. The method involves operating at least one processor to: define a bounding box surrounding a geographical zone; iteratively partition the bounding box into a plurality of bounding boxes by, starting with the bounding box in the first iteration: determine whether the bounding box contains more than a predetermined maximum number of vertices; divide the bounding box into two additional bounding boxes if the bounding box contains more than the predetermined maximum number of vertices, repeat the steps of determining and dividing the bounding box for the additional bounding boxes in the next iteration until each additional bounding box does not contain more than the predetermined maximum number of vertices; generate a binary tree data structure representing the geographical zone; search the binary tree data structure to determine whether a vehicle is located within one of the bounding boxes.