Anomaly Detection for Vehicle Calibration Sets
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Solution Overview
Problem
Existing systems for vehicle calibration are inefficient in detecting anomalies in large datasets, often requiring manual comparison of calibration parameters, which is time-consuming and prone to error.
Innovation Solution
A method for detecting anomalies in vehicle calibration sets involves receiving multiple calibration sets, calculating anomaly scores for each parameter, and modifying parameters based on detected anomalies, with the ability to transmit these modifications to vehicle control modules via Over-The-Air updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of calibration parameters is used, then detection accuracy can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical comparison of calibration parameters with an automated computer-based system that uses machine learning models and algorithms to detect anomalies. The system automatically compares calibration parameters across multiple vehicles, calculates anomaly scores, and identifies deviations without human intervention, thereby eliminating time consumption and labor intensity while maintaining or improving detection accuracy through sophisticated computational analysis.
Solution Approach 2:
The patent introduces an intermediary anomaly detection system that acts as a mediator between calibration data collection and manual review. This system includes components for automated data processing, machine learning-based anomaly scoring, and prioritization algorithms that filter and rank potential anomalies. The intermediary system pre-processes large volumes of calibration data, presenting only the most significant anomalies to human reviewers, thus dramatically reducing the time and effort required for complete calibration verification.
2Reliability
If comprehensive calibration checking is performed on all parameters, then detection reliability improves, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the comprehensive calibration checking process into distinct modular components: data collection modules that gather calibration parameters from multiple vehicles, preprocessing modules that clean and standardize the data, anomaly detection modules that apply machine learning models to identify deviations, and reporting modules that present results. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive coverage of all calibration parameters for high reliability.
Solution Approach 2:
The patent implements partial action by using machine learning models to identify and prioritize only the most significant anomalies rather than requiring manual review of every calibration parameter. The system calculates anomaly scores for all parameters but focuses detailed analysis and human review only on parameters exceeding predefined thresholds or showing highest anomaly scores. This approach maintains high detection reliability for critical issues while reducing computational complexity and resource requirements by avoiding exhaustive analysis of all parameters.
3Productivity
If automated anomaly detection is implemented, then productivity increases, but false positive rates may increase
Solution Approach 1:
The patent implements feedback mechanisms where the anomaly detection system continuously learns from confirmed and rejected anomalies. When human reviewers verify or reject automated anomaly detections, this feedback is used to retrain and refine the machine learning models, adjusting sensitivity thresholds and improving detection accuracy. The system adapts its anomaly scoring algorithms based on historical data and validation results, thereby maintaining high productivity through automation while progressively reducing false positive rates through iterative improvement driven by feedback loops.
Data Source
AI summary
A method for detecting anomalies in vehicle calibrations sets includes receiving a plurality of vehicle calibration sets, each vehicle calibration set including corresponding vehicle parameters, calculating, for each vehicle parameter of each vehicle calibration set, an anomaly score, detecting an anomaly associated with at least one vehicle parameter of a vehicle calibration set of the plurality of vehicle calibration sets based on the anomaly score for the vehicle parameter and a defined threshold, in response to detecting the anomaly, modifying the vehicle parameter of the vehicle calibration set, and transmitting the vehicle calibration set including the modified vehicle parameter to a vehicle control module associated with a vehicle for controlling at least one component of the vehicle. Other example methods and systems for detecting anomalies in vehicle calibrations sets are also disclosed.