Confidence Parameter Stabilization for Vehicle Classification
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Solution Overview
Problem
Conventional classification methods face high risks of misclassification and instability, especially in borderline cases, due to measurement errors and the inability to provide differentiated classification results, which is inadequate for situations requiring precise hazard estimation, such as vehicle surroundings analysis.
Innovation Solution
A method that calculates a confidence parameter from quality measurements over time, stabilizes classification results by filtering out outliers, and adjusts confidence values based on previous classifications, allowing for more differentiated evaluations and robustness against disruptions, while incorporating absolute quality and weighted reliability of individual results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional classification methods are used for object allocation, then classification speed is maintained, but misclassification risk increases in borderline cases
Solution Approach 1:
The patent applies preliminary action by performing multiple preclassifications before reaching a final classification decision. The system conducts several classification attempts with different parameters and compares results, rather than relying on a single classification pass. This preliminary multi-angle assessment reduces misclassification risk in borderline cases by evaluating objects from multiple classification perspectives before final allocation.
Solution Approach 2:
The patent implements feedback mechanisms by comparing results from multiple preclassifications and using this comparison information to adjust the final classification decision. The system feeds back the results of individual preclassifications into a综合 evaluation process, where discrepancies between preclassifications trigger further analysis or alternative classification paths, thereby improving reliability without excessive complexity.
2Stability of the object's composition
If single-timepoint classification is performed, then processing efficiency is maintained, but result stability deteriorates due to measurement errors
Solution Approach 1:
The patent performs preliminary classifications at multiple time points or with multiple measurement sets before finalizing the classification. Rather than relying on a single measurement, the system conducts several preclassifications with different data samples or at different moments, then synthesizes these results to achieve stable classification that is robust against individual measurement errors or outliers.
Solution Approach 2:
The patent changes classification parameters across multiple preclassifications, using different feature sets, thresholds, or algorithmic approaches for each preclassification attempt. By varying parameters and comparing results across multiple attempts, the system identifies consistent classification outcomes that are stable against parameter variations and measurement noise.
3Loss of information
If yes/no binary classification is used, then simplicity is maintained, but information differentiation is lost for hazard estimation
Solution Approach 1:
The patent applies local quality by providing differentiated classification information for different object classes and different aspects of classification confidence. Rather than a uniform yes/no answer, the system provides class-specific confidence levels, multiple possible class rankings, and context-dependent classification details that are tailored to the specific object and classification context, thereby preserving information without requiring complete system complexity.
Solution Approach 2:
The patent performs preliminary classifications that generate rich information about object attributes, class memberships, and confidence levels before finalizing the classification output. These preclassifications produce detailed intermediate results including multiple candidate classes, confidence scores, and attribute assessments that preserve information for subsequent hazard estimation and decision-making processes.
4Stability of the object's composition
If individual classification results are used without temporal integration, then responsiveness is maintained, but classification stability in borderline cases deteriorates
Solution Approach 1:
The patent performs preliminary classifications at multiple time points or with multiple measurement sets before finalizing the classification decision. Rather than relying on a single measurement, the system conducts several preclassifications with different data samples or at different moments, then synthesizes these results to achieve stable classification that is robust against individual measurement errors or outliers.
Data Source
AI summary
Classification methods are described that proceed in computer-assisted fashion, and in particular a method for evaluation and stabilization over time of classification results is described in which objects to be classified are sensed using sensors over a period of time, and are repeatedly classified with the inclusion of specific quality parameters for each object class. To ensure better classification reliability, the following steps may be carried out: a) increasing the value of the confidence parameter if a subsequent classification confirms the result of a previous classification; b) decreasing the value of the confidence parameter if a subsequent classification does not confirm the result of a previous classification; and c) generating a final classification result including the confidence parameters that have been increased or decreased in value.