Temporal Corroboration Scoring for GNSS-Spoofed Map Updates
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Solution Overview
Problem
Large-area GNSS spoofing and/or jamming cause crowd-sourced data to be inaccurate, leading to misrepresentation of observed features in digital maps, as conventional methods fail to detect and mitigate such manipulation.
Innovation Solution
A method to determine corroboration scores for crowd-sourced data based on location estimates and observation data, comparing these scores across time periods to identify potential manipulation, and taking mitigating actions such as reverting or delaying updates to the digital map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If crowd-sourced data is used to update digital maps, then map updates are frequent and current, but accuracy deteriorates due to GNSS spoofing and jamming manipulation
Solution Approach 1:
The system performs preliminary validation of crowd-sourced data by computing corroboration scores before incorporating data into map updates. This preliminary action filters out potentially manipulated data while preserving legitimate updates, resolving the contradiction between update frequency and accuracy
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring corroboration scores and comparing them against thresholds. When scores indicate potential manipulation, the system adjusts its data acceptance criteria, creating a closed-loop system that maintains accuracy while allowing frequent legitimate updates
2Measurement precision
If corroboration scoring is implemented to detect manipulation, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The corroboration scoring system uses the existing digital map data itself to validate new crowd-sourced data. The system checks whether observed features in new data match the established map, creating a self-validating system that improves accuracy without requiring external validation infrastructure
Solution Approach 2:
The system transforms the validation problem into a scoring parameter that can be computed efficiently. By converting complex validation logic into a quantitative corroboration score with clear thresholds, the system maintains high validation accuracy while reducing computational complexity
3Measurement precision
If potentially manipulated data is rejected, then map accuracy is maintained, but data utilization efficiency decreases
Solution Approach 1:
The system applies different acceptance criteria to different data based on their corroboration scores. Rather than uniformly rejecting all potentially manipulated data, the system selectively accepts data that meets local quality thresholds, maintaining accuracy while maximizing utilization of valid crowd-sourced data
Data Source
AI summary
An apparatus obtains instances of crowd-sourced data corresponding to a first time period and determines a respective corroboration score based on map data a respective instance of crowd-sourced data. The apparatus determines a first representative corroboration score for the first time period based on respective corroboration scores determined for the instances of crowd-sourced data corresponding to the first time period; and compares the first representative corroboration score for the first time period to a second representative corroboration score corresponding to a second time period, wherein the first time period is different from the second time period. Based on a result of comparing the first and second representative corroboration scores, the apparatus determines whether the instances of crowd-sourced data are potentially manipulated. Responsive to determining that the instances of crowd-sourced data are potentially manipulated, the apparatus performs at least one mitigating action.