Method and device for assigning identification information and identification data to a vehicle
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Solution Overview
Problem
Existing traffic monitoring systems face challenges in reliably and unambiguously identifying vehicles, particularly in congested conditions or during overtaking maneuvers, leading to low assignment reliability and imprecise binary decisions.
Innovation Solution
A method and device that utilize image data from cameras and transmitter units to assign identification information and data to vehicles by synchronizing timestamps and allowing for a tolerance range in license plate matching, with deferred assignments if timing or matching criteria are not met, ensuring accurate vehicle identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple vehicles are detected within the monitoring area using existing systems, then vehicle detection coverage is improved, but assignment reliability deteriorates due to ambiguities that cannot be reliably resolved
Solution Approach 1:
The system uses feedback loops where identification information and data are continuously compared against stored records. If the time difference exceeds the threshold or matching fails, the system stores the data for later assignment rather than making an immediate incorrect assignment, thereby improving reliability through iterative verification
Solution Approach 2:
The system performs preliminary actions by storing identification information and data for subsequent assignment when immediate assignment criteria are not met. This preliminary storage allows for later resolution when more information becomes available, preventing premature incorrect assignments
2Speed
If existing systems make binary decisions on vehicle identification, then decision speed is improved, but precision deteriorates due to inability to represent ambiguous states
Solution Approach 1:
The system transitions from static binary decisions to a dynamic multi-state evaluation process. It continuously evaluates time differences and matching criteria, allowing the identification state to evolve from uncertain to confirmed as more data becomes available, thereby improving precision without sacrificing speed
Solution Approach 2:
The system changes the parameter of decision states from binary (normal/abnormal) to multi-state by introducing time difference thresholds and tolerance ranges. This allows for nuanced representation of identification confidence levels, improving precision while maintaining efficient processing
3Measurement precision
If strict matching criteria are applied for license plate and characteristic verification, then identification precision is improved, but assignment success rate deteriorates due to tolerance issues
Solution Approach 1:
The system applies local quality by implementing tolerance ranges specifically for license plate and characteristic matching. Rather than applying uniform strict criteria, it allows for localized flexibility in character comparison while maintaining precision in time synchronization, thereby balancing precision with success rate
Data Source
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AI summary
The approach set forth here relates to a method (200) for assigning identification information (105) of at least one vehicle (110) in a monitoring region (115) of a monitoring device (117) and identification data (120) to the vehicle, wherein the identification data (120) have been received from a transmission unit (135) of the vehicle (110). The method (200) comprises a step of reading in (210) the identification information (105) and the identification data (120), wherein the identification information (105) have been obtained from image data (131) of an image (129), captured by the monitoring device (117), of the vehicle (110) traveling in the monitoring region (115) and the identification data (120) have been received from a transmission unit (135) of the vehicle (110), wherein the identification information (105) is assigned an information timestamp (155) representing the time of reading-in (210) of the identification information (105) and the identification data (120) are assigned a data timestamp (163) representing the time of reception of the identification data (120), wherein, in the reading-in step (210), an identifier (172) uniquely representing the vehicle is furthermore read into the identification information (105) and a characterizing feature (174) uniquely representing the vehicle (110) is read into the identification data (120). The method (200) furthermore comprises a step of assigning (220) the identification information (105) and the identification data (120) to the vehicle (110) when a time difference between the information timestamp (155) and the data timestamp (163) is not greater than a threshold value time difference and when the identifier (172) and the characterizing feature (174) match to within a tolerance range. The method (200) furthermore comprises a step of storing the identification information (105) and the identification data (120) for assignment of the identification information (105) and the identification data (120) to the vehicle (110) at a later time when a time difference between the information timestamp (155) and the data timestamp (163) is greater than a threshold value time difference or a step of storing the identification information (105) and the identification data (120) for assignment of the identification information (105) and the identification data (120) to the vehicle (110) at a later time when the identifier (172) and the characterizing feature (174) do not match to within a tolerance range.