Map Matcher Tolerant to Wrong Map Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current map-matching systems face challenges in accuracy, scalability, and efficiency when processing high volumes of probe data from less reliable sources, particularly due to noise and errors in probe data and outdated map features.
Innovation Solution
A robust path-based map matching system that identifies and corrects wrong map features by analyzing probe trajectories, using turning point detection and feasibility checks to improve accuracy and robustness, even with noisy or outdated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional map-matching systems process high volumes of probe data from less reliable sources, then the quantity of processed data increases, but accuracy deteriorates due to noise and errors in probe data and outdated map features
Solution Approach 1:
The probe trajectory is divided into multiple segments based on turning points detected from low-speed probe points. Each segment is processed independently to identify wrong map features, allowing the system to handle large volumes of data while maintaining accuracy through localized analysis rather than processing entire trajectories at once
Solution Approach 2:
The system performs preliminary processing by detecting turning points and segmenting trajectories before conducting feasibility checks. This preliminary action identifies potential wrong map features early in the process, enabling subsequent steps to focus computational resources on verifying and correcting specific suspected errors rather than processing all data uniformly
2Measurement precision
If the system performs comprehensive feasibility checks and turning point detection to improve accuracy, then map matching precision improves, but computational complexity increases
Solution Approach 1:
The feasibility check focuses computational effort locally at turning points and segment boundaries where wrong map features are most likely to occur. Rather than applying complex checks uniformly across entire trajectories, the system concentrates analysis at critical locations, improving accuracy where needed while reducing overall computational complexity
Solution Approach 2:
By dividing trajectories into segments based on turning points, the system reduces computational complexity through localized processing. Each segment can be independently analyzed with simplified feasibility checks, avoiding the need to process entire long trajectories with full computational complexity
3Duration of action of stationary object
If the system uses outdated map data for matching, then the availability of map data is maintained, but reliability deteriorates due to wrong map features from continuous changes in road networks
Solution Approach 1:
The system performs feasibility checks that provide feedback on whether matched map features are correct. When inconsistencies are detected at turning points or segment boundaries, the system identifies wrong map features and can trigger updates. This feedback mechanism allows the system to maintain reliability even when using map data that may contain outdated features
Solution Approach 2:
The system converts the potential harm of using outdated map data into a benefit by using the inconsistencies between probe trajectories and map features as detection signals. Wrong map features cause detectable anomalies in the matching process, which the system uses to identify and correct errors, ultimately improving map data quality
Data Source
AI summary
An approach is provided for providing a map matcher tolerant to wrong map features. The approach involves, for instance, finding a segment of a probe trajectory containing a plurality of low-speed probe points. The approach also involves forming a line between a first and a last probe point of the segment. The approach further involves calculating a distance from each probe point of the segment to the line. The approach further involves splitting the segment based on comparing the distance to a threshold at a turning point of the segment. The approach further involves creating a new probe trajectory based on a plurality of high-speed probe points in the probe trajectory and the turning point. The approach further involves estimating a heading of the turning point of the segment based on the new trajectory, and then performing a feasibility check between two consecutive probe points of the new trajectory.