Crowd-Sourced Probe Data Reliability for Region-Based Map Updates
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Solution Overview
Problem
The reliability of crowd-sourced probe data varies widely, making it difficult to determine which data is trustworthy, especially in regions with inaccurate GPS signals or sparse satellite coverage, which affects map updates and autonomous vehicle control.
Innovation Solution
An apparatus and method that assess the reliability of probe data by determining location accuracy and sensor reliability for each probe apparatus, using regions of trust to validate sensor data and update map databases, and facilitating autonomous vehicle control with synthetic generation of sensor data for new configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If crowd-sourced probe data is used for map updates, then map data coverage and update frequency are improved, but data reliability and accuracy deteriorate due to varying sensor quality and GPS signal accuracy
Solution Approach 1:
The system divides the geographic area into multiple regions and assigns different reliability weights to probe data based on the specific region where the data was collected. Regions with good GPS coverage receive higher reliability weights, while regions with poor GPS coverage receive lower weights. This allows the system to utilize crowd-sourced data efficiently while accounting for local variations in data quality.
Solution Approach 2:
The system dynamically adjusts the reliability parameter of probe data based on multiple factors including GPS signal quality, sensor type, sensor calibration status, and historical performance metrics. By changing the reliability parameter according to these conditions, the system can flexibly weigh different data sources and maintain overall data quality while processing large volumes of crowd-sourced information.
2Quantity of substance
If probe data from all regions is accepted, then data quantity and coverage are improved, but data quality deteriorates due to inaccurate GPS signals in certain regions
Solution Approach 1:
The system implements region-specific quality assessment by categorizing geographic areas into trust regions and non-trust regions based on GPS signal characteristics. Probe data from trust regions is assigned higher location accuracy weights, while data from non-trust regions receives lower weights or undergoes additional validation. This enables the system to accept diverse data quantities while maintaining overall measurement precision through differential weighting.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously evaluate GPS signal quality and sensor performance metrics. Based on this feedback, the system adjusts the acceptance criteria and weighting of probe data in real-time. Regions with consistently poor signal quality are identified, and future data from these regions is automatically weighted lower or subjected to stricter validation, thereby maintaining data quality while preserving data quantity from acceptable sources.
3Measurement precision
If manual validation of probe data is performed, then data accuracy is improved, but processing time and operational complexity increase
Solution Approach 1:
The system implements automated self-validation mechanisms that evaluate probe data reliability without manual intervention. The system automatically assesses GPS signal quality, sensor calibration status, and data consistency metrics, then assigns reliability weights accordingly. This self-service approach maintains high data accuracy through systematic validation while eliminating the time loss and operational complexity associated with manual review processes.
Solution Approach 2:
The system dynamically changes validation parameters based on data source characteristics and regional conditions. For highly reliable data sources in trust regions with good GPS coverage, the system applies streamlined validation with lower computational overhead. For data from uncertain sources or non-trust regions, the system automatically applies more stringent validation parameters. This adaptive parameter adjustment maintains data accuracy while optimizing processing time by avoiding unnecessary validation steps for high-quality data.
Data Source
AI summary
A method, apparatus and computer program product are provided for establishing the reliability of crowd sourced data based on the source of the data and the region in which the data is gathered. Methods may include: receiving map data for a network of roads in a geographic area; receiving location accuracy data from different regions within the geographic area, where the location accuracy data for each region may be indicative of an accuracy with which location can be established in the respective region; receiving a plurality of probe data points; determining, for each probe data point, the location accuracy data associated with the location information associated with the respective probe apparatus; determining, for each probe apparatus, a reliability of the one or more sensors; and updating a map database based on at least one probe data point.