Road Event Data Accuracy via Historical Offset Filtering
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Solution Overview
Problem
Current systems face challenges in accurately and efficiently updating construction zone location data due to location sensor errors, digital map errors, and inconsistencies between providers, leading to delays and additional processing costs.
Innovation Solution
A method and apparatus for collecting and maintaining road event information by receiving road event data indicating a road event on a representation of a road, converting road event links into categorized offsets, and identifying road event categorized offsets to provide accurate and timely updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional processing is performed to correct construction zone location data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary correction by comparing incoming construction zone data with historical data from the same geographic location to establish expected patterns. This preliminary action filters out obvious errors before final processing, reducing the need for time-consuming additional corrections while maintaining accuracy.
Solution Approach 2:
The system uses feedback from historical construction zone data to validate and correct incoming data. By comparing current data with previously identified construction zones at the same location, the system can automatically correct location inaccuracies without manual intervention, resolving the contradiction between precision and time loss.
2Reliability
If additional processing is performed to correct road event data, then reliability is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary correction by comparing incoming construction zone data with historical data from the same geographic location to establish expected patterns. This preliminary action filters out obvious errors before final processing, reducing the need for time-consuming additional corrections while maintaining accuracy.
Solution Approach 2:
The system performs self-correction by automatically comparing incoming data with historical data and adjusting location information without external intervention. This self-service capability maintains high reliability while improving productivity by eliminating manual processing steps.
3Measurement precision
If location sensor errors and digital map errors are corrected, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses historical construction zone data as an intermediary to mediate between incoming sensor data and digital map data. This intermediary layer simplifies the correction process by providing reference information that automatically resolves discrepancies without requiring complex real-time processing algorithms.
Solution Approach 2:
The system creates a copy of historical construction zone data at the same geographic location and uses this copy to validate and correct incoming data. This copying approach simplifies error correction by comparing against a known good reference rather than attempting complex real-time validation.
Data Source
AI summary
A method and system are disclosed for collecting and maintaining road event information, where point-based road event location data from transportation authorities, construction companies, vehicle sensors, or combinations thereof, is susceptible to location sensor errors, digital map errors, and/or map mismatching errors. Errors less than a selected threshold are filtered by categorizing or grouping reports of point-based locations into segments along links in a representation of a road, providing improved accuracy of reporting of road events.