Toll System Gap Detection Plausibility Check
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Solution Overview
Problem
Current toll systems face challenges in reliably distinguishing between genuine gaps in route section identifiers when driving outside toll roads and false gaps due to errors within the toll network, particularly during breaks or changes in road network conditions, which can lead to incorrect toll calculations and data protection issues.
Innovation Solution
A method and device that utilize vehicle movement parameters, such as limit speed duration, to perform plausibility checks on gap sequences by comparing decentralized and centralized data to identify genuine and false gaps, without requiring absolute position data, and differentiate between software and hardware errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gap sequences are detected by comparing route section identifiers without additional checks, then the detection process is simple and fast, but false gaps are incorrectly identified when vehicles take toll-free alternative routes or experience system errors
Solution Approach 1:
The detection system is segmented into multiple independent checking components: first plausibility check (vehicle movement parameters), second plausibility check (error frequency analysis), and third plausibility check (gap frequency analysis). Each segment performs a specific verification function, allowing the system to maintain high reliability while keeping individual components manageable in complexity
Solution Approach 2:
Multiple intermediary checks are introduced between the simple gap detection and the final error determination. These include vehicle movement parameter verification, error frequency thresholds, and gap frequency thresholds that act as mediators to filter out false positives before concluding a genuine error exists
2Measurement precision
If multiple plausibility checks with frequency thresholds are implemented, then false positives are reduced and detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial checking by using frequency thresholds that only trigger detailed analysis when gaps exceed certain frequencies. Not all gap sequences undergo the full three-level plausibility check - only those that meet specific frequency criteria, thus reducing overall processing time while maintaining accuracy for problematic cases
Solution Approach 2:
Frequency thresholds and preliminary filters are established before the main detection process. By pre-defining what constitutes suspicious gap frequencies and error rates, the system can quickly eliminate normal variations without performing computationally intensive analysis, saving processing time
3Reliability
If vehicle movement parameters and frequency thresholds are used to distinguish genuine gaps from false gaps, then toll calculation accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system monitors changes in multiple parameters simultaneously: vehicle movement parameters (speed, distance, time), error frequencies, and gap frequencies. By tracking how these parameters change relative to each other and against predefined thresholds, the system can reliably distinguish genuine errors from normal variations without requiring complex analytical models
Data Source
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AI summary
To detect errors in a toll system, methods and devices are proposed that subject sequences of detected toll sections to a plausibility check, taking into account vehicle movement parameters that were recorded by a decentralized data processing device carried by the toll vehicle in connection with the detection of the toll sections. According to the invention, the vehicle movement parameter is a duration that is the sum of partial durations during which the vehicle speed exceeds a limit speed.