V2X Location Data Fusion and Filtering
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Solution Overview
Problem
In vehicle-to-everything (V2X) systems, there is a need to effectively group and identify unique location data from various sources with different accuracy levels to enhance road safety, as existing methods struggle to distinguish between overlapping and unique data points from sources like vehicle on-board diagnostic devices, roadside cameras, and GPS systems.
Innovation Solution
The system determines confidence levels for location data based on source accuracy and applies filtering techniques to prioritize and share only the most accurate data, using Real-Time Kinematic (RTK) data as the highest confidence level, and suppressing redundant lower-accuracy data to ensure accurate vehicle tracking and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If location data from multiple sources with different accuracy levels is collected and shared, then the quantity of location information increases, but the reliability of the data sharing system deteriorates due to redundant and less accurate data
Solution Approach 1:
The system changes the parameter of data accuracy by assigning different confidence levels (e.g., high, medium, low) to location data from different sources. This allows the system to differentiate and prioritize more accurate data (such as RTK data) over less accurate data, resolving the contradiction by enabling quantity increase while maintaining reliability through parameter-based filtering
Solution Approach 2:
The system applies local quality by treating different location data sources with different accuracy characteristics uniquely. Instead of uniform processing, each data source is evaluated based on its specific accuracy level, and filtering decisions are made locally for each data point based on its confidence level and redundancy status
2Loss of information
If all location data from multiple sources is shared without filtering, then the completeness of information is improved, but the device complexity increases due to the need to process and manage redundant data
Solution Approach 1:
The system extracts only the essential and non-redundant location data from multiple sources by applying confidence level filtering. This extraction process removes unnecessary duplicate data while preserving unique and accurate information, thereby reducing processing complexity without sacrificing information completeness
Solution Approach 2:
The system segments location data into different confidence level categories (high, medium, low) and processes each segment differently. This segmentation allows the system to manage data complexity by handling only relevant segments while maintaining completeness of essential information
3Measurement precision
If redundant lower-accuracy data is suppressed in favor of higher-accuracy data, then the measurement precision is improved, but the loss of information increases due to potential elimination of unique data points
Solution Approach 1:
The system dynamically adjusts data retention decisions based on real-time analysis of data redundancy and uniqueness. Instead of static filtering, the system evaluates each data point's contribution to overall information completeness while prioritizing precision, allowing adaptive balancing between precision and information loss
Data Source
AI summary
Systems and methods limit communication of redundant telematics data in V2X systems. A communications management device receives telematics data from multiple sources in a service area and calculates a trajectory each of the objects identified by the telematics data. The communications management device selects high vulnerability trajectories based on the calculated trajectories and identifies when the telematics data from different sources, of the multiple sources, corresponds to a same vehicle. When duplicate sources are determined to provide tracking data corresponding to the same vehicle, the communications management device reports (to a collision avoidance system) the tracking data from only the most accurate of the duplicate sources.