Connected Vehicle MAP Data Correction via Traffic Flow Analysis
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Solution Overview
Problem
Current traffic light analysis systems face challenges with labor-intensive and inaccurate MAP data retrieval, which can lead to incorrect conclusions when combined with SPAT data, posing risks to drivers and passengers.
Innovation Solution
A method utilizing connected vehicles to automatically derive or correct MAP data by comparing real-time traffic flow data with geometry and topology data, allowing for real-time updates and detection of discrepancies, thereby providing accurate information for traffic flow control devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MAP data is manually retrieved from drawings and technical documentation, then the data can be obtained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables automatic retrieval and verification of MAP data through connected vehicles that self-report their position and trajectory information. The vehicles themselves provide the mapping data needed for traffic light analysis, eliminating manual retrieval processes
Solution Approach 2:
The system continuously compares real-time vehicle position data with predicted positions based on SPAT data and existing MAP data. This feedback loop automatically detects discrepancies and triggers MAP data updates without manual intervention, reducing both time and labor requirements
2Reliability
If MAP data is updated manually after infrastructure reconstructions, then the data can be corrected, but the process remains labor-intensive and prone to errors
Solution Approach 1:
The system continuously monitors vehicle positions and compares them against predicted positions using SPAT and MAP data. When discrepancies are detected that indicate infrastructure changes, the system automatically flags and corrects MAP data errors, maintaining high reliability without manual effort
Solution Approach 2:
The patent replaces manual mechanical processes of retrieving and updating MAP data from drawings with an automated electronic system that uses vehicle sensor data, GPS information, and algorithmic comparison to automatically detect and correct mapping errors
3Productivity
If inaccurate MAP data is used with SPAT data for traffic light prediction, then the system can function, but driver safety is compromised due to incorrect conclusions
Solution Approach 1:
The system continuously compares actual vehicle positions and traffic flow patterns against predictions made using SPAT and MAP data. This feedback mechanism detects when MAP data becomes inaccurate and automatically triggers corrections, ensuring driver safety is maintained while preserving system functionality
Solution Approach 2:
The system performs preliminary verification of MAP data accuracy by comparing it with real-time vehicle position data before using the data for traffic light predictions. This preliminary check prevents incorrect conclusions from being drawn, thereby protecting driver safety
Data Source
AI summary
A method is described for performing a real time analysis of traffic flow control device related data using a plurality of vehicles connected in at least one vehicle cell network. The method compares position data from vehicles defining a traffic flow with received data for a traffic flow control device and geometry and topology data concerning a transport engineering construction related to the traffic flow control device to evaluate if a discrepancy can be detected between the geometry and topology data concerning the transport engineering construction and the traffic flow defined by the vehicles positions. The method may be used for updating and/or finding errors in the geometry and topology data concerning the transport engineering construction.