Generative AI Automotive Diagnosis Predicting Error Conditions
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Solution Overview
Problem
Modern vehicles with complex electronics and computing systems present challenges in accurately and efficiently diagnosing issues, as error codes and driver symptom descriptions often lack context, leading to methodological troubleshooting.
Innovation Solution
The use of generative artificial intelligence models to predict automotive diagnoses by processing symptomatic data, including error codes and user descriptions, and generating predicted error conditions, along with confidence scores and repair procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methodological troubleshooting is used to diagnose vehicle issues, then technicians can systematically work through problems, but the process becomes time-consuming and less efficient
Solution Approach 1:
The system performs preliminary diagnostic actions by pre-processing symptomatic data and error codes through machine learning models before the actual diagnosis begins. The model predicts potential error conditions and repair procedures in advance, allowing technicians to start with targeted information rather than systematic guessing, thus reducing troubleshooting time while maintaining diagnostic thoroughness
Solution Approach 2:
The machine learning model acts as an intermediary between the raw symptomatic data (error codes, driver descriptions) and the final diagnosis. It processes and interprets the unstructured data, generating predicted error conditions and repair procedures that bridge the gap between initial symptoms and technical diagnosis, thereby accelerating the troubleshooting process without sacrificing accuracy
2Loss of information
If error codes are used for vehicle diagnosis, then diagnostic information is captured, but the codes provide little to no context without additional information
Solution Approach 1:
The system creates an enriched copy of the diagnostic information by generating contextual explanations alongside error codes. The machine learning model produces detailed descriptions of what the error codes mean in context, what components are likely affected, and what repair procedures are needed, effectively copying and expanding the raw diagnostic data into actionable information without increasing the complexity of data collection
Solution Approach 2:
The machine learning model serves as an intermediary that translates raw error codes and symptomatic data into contextualized diagnostic information. It processes the unstructured data, identifies patterns, and generates human-readable explanations that provide context about the vehicle issue, thereby reducing the complexity of data interpretation while maintaining comprehensive diagnostic information
3Adaptability or versatility
If driver symptom descriptions are used for diagnosis, then user perspective is captured, but the descriptions can be unhelpful and direct technicians in the wrong direction
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously refines its predictions based on the relationship between driver symptom descriptions and actual diagnostic data. The model learns from patterns in the data to better interpret and translate subjective symptom descriptions into accurate diagnostic predictions, thereby improving reliability while maintaining adaptability to various user perspectives
Solution Approach 2:
The system changes the parameter representation of driver symptom descriptions by transforming them from subjective text into structured diagnostic parameters. The machine learning model processes the natural language descriptions and converts them into standardized diagnostic categories and error conditions, thereby improving the reliability of diagnosis while maintaining the adaptability to capture diverse user perspectives
Data Source
AI summary
An artificial intelligence (AI) diagnostic display device for predicting automotive diagnosis. The AI diagnostic display being configured to: receive a set of symptomatic data of the vehicle relating to detected conditions of the vehicle, the symptomatic data including: (i) one or more symptoms comprising (A) one or more generated error codes from the vehicle and/or (B) one or more symptomatic descriptions of the vehicle from a user and (ii) date data of when each symptom was detected; receive a set of repair or maintenance procedures of the vehicle relating to addressing each symptom in the set of symptomatic data; input the set of symptomatic data and the set of repair or maintenance procedures into the generative diagnostic prediction machine learning model to generate one or more predicted error conditions of the vehicle; and/or present, via an interactive display, at least a portion of the one or more predicted error conditions.