Map Matching Using Iterative Radius Search
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Solution Overview
Problem
Current map matching techniques for probe data are computationally expensive and inefficient, particularly for large datasets, as they require a separate spatial search for each probe point, leading to substantial execution time and costs, especially in real-time applications.
Innovation Solution
A method that iteratively increases the search radius for candidate road segments or nodes for each probe data point until a maximum radius is reached or multiple candidates are found, allowing for map matching and path generation based on these candidates, and updates map data for route navigation or semi-autonomous driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate spatial search is conducted for each probe point to identify the closest road segment, then map matching accuracy is improved, but computational expense and execution time increase substantially
Solution Approach 1:
The patent segments the probe data processing by dividing probe points into different groups based on their spatial distribution and road network characteristics. By processing segments of probe data rather than individual points in isolation, the system reduces the total number of spatial searches required while maintaining matching accuracy through segment-level optimization strategies.
Solution Approach 2:
The patent performs preliminary actions by pre-processing probe data to identify and group spatially proximate probe points before conducting spatial searches. This preliminary grouping allows the system to conduct fewer, more efficient spatial searches that cover multiple probe points simultaneously, thereby reducing execution time while preserving accuracy.
2Measurement precision
If the search radius is increased to capture probe points outside conventional matching boundaries, then map matching accuracy for noisy data is improved, but computational expense increases
Solution Approach 1:
The patent implements a dynamic search radius that adjusts based on local conditions such as probe point density, road segment characteristics, and data noise levels. Rather than using a fixed large radius for all probe points, the system dynamically determines the appropriate search radius for each probe point or group, capturing noisy data points that fall outside conventional boundaries while avoiding unnecessary computational expense in low-noise areas.
Solution Approach 2:
The patent applies different search strategies and radius parameters to different local regions based on their specific characteristics. In areas with high data noise or sparse road networks, the system increases the search radius and applies more rigorous matching criteria, while in areas with clean data and dense road networks, it uses conventional parameters. This local quality approach ensures accurate matching for noisy data without uniformly increasing computational expense across all regions.
3Measurement precision
If post-processing is used to reference future probe points for map matching, then map matching accuracy is improved, but real-time application capability is lost
Solution Approach 1:
The patent applies partial post-processing by referencing only a limited number of future probe points rather than all subsequent points in the dataset. This partial action provides enough lookahead information to improve accuracy for current probe points while limiting the computational burden, thereby enabling near real-time processing capabilities that balance accuracy improvements with processing speed requirements.
Data Source
AI summary
A method map matches probe data to a candidate road segment or node. Methods may include: searching for candidate road segments or nodes for each probe data point to be matched to, where searching for candidate road segments or nodes includes: searching within a predefined radius of each probe data point for road segments or nodes and in response to no road segments or nodes being found within the predefined radius of the respective probe data point, iteratively increasing the predefined radius and searching again until a predefined maximum radius is reached or at least two road segment candidates or node candidates are found; map matching each probe data point to a respective road segment candidate or node candidate based on the road segment candidate or node candidate found in the search; and generating a path based on the map matched probe data points from a respective probe.