ADAS Calibration Identification via Machine Learning Analysis
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Solution Overview
Problem
Current methods for identifying vehicle components that require calibration after a collision are inefficient and costly, relying heavily on manual processes and proprietary data, which become increasingly complex and difficult to manage as vehicles become more advanced, leading to a need for automated and generic solutions that can identify ADAS systems across various manufacturers.
Innovation Solution
A system and method utilizing machine learning models, including natural language processing and neural networks, to analyze diagnostic codes and repair data, generating outputs in a human-readable format to determine which vehicle components require calibration, leveraging Golden DTCs and CEICA codes to identify specific sensor needs, and providing scalable solutions for calibration identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to identify vehicle components requiring calibration, then human judgment and analysis can be applied, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated machine learning system. The machine learning model processes diagnostic codes, repair orders, and vehicle data automatically to identify calibration requirements, eliminating the need for manual human analysis while maintaining accuracy through trained algorithms that recognize patterns across multiple data sources.
Solution Approach 2:
The system enables self-service calibration identification by automatically analyzing vehicle data and diagnostic information without requiring manual intervention. The machine learning model independently processes input data, identifies calibration needs, and provides recommendations, allowing the system to serve itself rather than requiring continuous human oversight for basic identification tasks.
2Measurement precision
If proprietary build data is used to identify sensors for calibration, then specific vehicle information can be obtained, but the method becomes difficult to generalize across different manufacturers
Solution Approach 1:
The patent creates a universal machine learning system that can handle multiple manufacturers and vehicle types through a single integrated model. The system processes various data formats including diagnostic codes, repair orders, and vehicle specifications from different manufacturers, enabling it to generalize calibration identification across the entire automotive industry without requiring manufacturer-specific proprietary systems.
Solution Approach 2:
The system adapts to different manufacturers by dynamically adjusting its analysis parameters and data processing approaches. The machine learning model can interpret different diagnostic code formats, repair order structures, and vehicle data specifications from various manufacturers, changing its internal parameters and processing methods to match each manufacturer's specific data format while maintaining consistent calibration identification accuracy.
3Manufacturing precision
If detailed build data is manually analyzed to identify ADAS sensors, then specific sensor requirements can be determined, but the complexity increases as vehicles become more complex
Solution Approach 1:
The patent replaces complex manual analysis of detailed build data with automated machine learning processing. The machine learning model automatically parses and analyzes complex vehicle data structures, diagnostic codes, and repair information, handling the complexity of modern ADAS systems without requiring manual intervention to manage the increasing number of sensors and data points.
Solution Approach 2:
The system segments the complex data analysis process into distinct manageable components. The machine learning model divides the analysis into separate processing stages: data collection from multiple sources, data cleaning and validation, pattern recognition through trained algorithms, and calibration recommendation generation. This segmentation allows the system to handle complex ADAS data systematically through multiple simplified processing steps rather than a single complex manual analysis.
4Reliability
If human operators manually identify calibration requirements, then judgment can be applied to complex scenarios, but productivity decreases as vehicles become more complex
Solution Approach 1:
The patent substitutes manual human analysis with automated machine learning processing to dramatically increase productivity. The machine learning model processes vehicle data and diagnostic information automatically at high speed, analyzing multiple data sources simultaneously without the limitations of human processing speed, while maintaining reliable calibration determination through trained algorithms that consistently apply correct identification criteria.
Data Source
AI summary
Systems, methods, and apparatuses are provided for evaluating the calibration requirements or calibration needs of one or more sensors of a vehicle. A subject matter expert and/or a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair. In some examples, a machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate.