Map Data Conflation for Accurate Lane and Object Updates
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Solution Overview
Problem
Existing digital maps suffer from inaccurate lane geometry and object data, which can impair route guidance and autonomous vehicle control due to missing or erroneous information, necessitating laborious manual corrections and costly human interventions.
Innovation Solution
A system and method for aggregating and conflating map data from various sources with confidence scores and clustering algorithms to generate accurate and up-to-date map features and objects, using probe data, satellite imagery, and LiDAR, minimizing manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If digital maps are continuously updated with data from multiple sources, then map data currency is improved, but data accuracy deteriorates due to missing or erroneous information from various sources
Solution Approach 1:
The system implements feedback loops where map data is continuously validated against multiple sources, and confidence scores are recalculated based on new observations. This allows the system to maintain current map data while correcting errors through iterative verification processes.
Solution Approach 2:
The patent introduces an intermediary conflation process that acts as a mediator between multiple data sources. This conflation engine reconciles conflicting information from various sources before incorporating it into the master map, preventing erroneous data from directly corrupting the map database.
2Measurement precision
If manual verification and correction of map data is performed, then map data accuracy is improved, but processing time and cost increase
Solution Approach 1:
The system performs self-verification by automatically validating map data against multiple sources and detecting inconsistencies. The conflation process autonomously resolves conflicts and corrects errors without requiring manual intervention, enabling the system to maintain high accuracy while operating at automated speeds.
Solution Approach 2:
The patent replaces manual mechanical verification processes with automated computational systems. Machine learning algorithms and automated conflation engines substitute human reviewers, maintaining accuracy while dramatically reducing processing time and eliminating the need for manual labor in map data validation.
3Loss of information
If multiple data sources are conflated together, then map data completeness is improved, but system complexity increases
Solution Approach 1:
The conflation system is segmented into modular components that handle different aspects of data integration separately. Each data source is processed through dedicated modules that extract, validate, and prepare data independently before combining results, making the complex conflation process more manageable and maintainable.
Solution Approach 2:
The patent implements a universal conflation framework that can handle multiple data sources with different formats and characteristics through a single integrated system. The conflation engine is designed to be multi-functional, accommodating various input types (satellite imagery, LiDAR, probe data) while maintaining a consistent processing approach.
Data Source
AI summary
A method is provided automatically creating map objects from data from various sources gathered within a geographical area using data aggregation and conflation. Methods may include: receiving observation data associated with a geographic area; identifying at least one observed object within the observation data, the at least one observed object including an observed location and an observed object type; determining at least one confidence score for the at least one observed object; estimating similarity between the at least one observed object and at least one other observed object; merging the at least one observed object and the at least one other observed object; conflating the at least one observed object and the at least one other observed object to obtain a conflated object; providing for storage of the conflated object; and providing for at least one of navigational guidance or at least semi-autonomous vehicle control using the stored, conflated object.