On-Board Diagnostics Data Validation via Cross-Source Correlation
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Solution Overview
Problem
Existing methods struggle to reliably validate the integrity of data from on-board diagnostics systems in vehicles, particularly distinguishing between authentic and manipulated data, which is crucial for applications like vehicle insurance and performance characterization.
Innovation Solution
A method involving cross-validation of data elements from an on-board diagnostics system with additional data sources, including off-vehicle sensors and other vehicles, using correlation rules and probability analysis to confirm data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data from OBD system is used directly without validation, then data accessibility is improved, but data reliability deteriorates due to potential manipulation
Solution Approach 1:
The patent introduces an intermediary validation system that sits between the OBD data source and the end user. This intermediary cross-validates OBD data against multiple independent sources including sensor data from the vehicle, historical data patterns, and external data sources. The validation system acts as a mediator that filters and verifies data authenticity before presenting it to users, thereby maintaining ease of data access while ensuring data reliability through multi-source verification
2Reliability
If multiple data sources are used for cross-validation, then data reliability is improved, but device complexity increases
Solution Approach 1:
The validation system is segmented into distinct functional modules: data collection module that gathers data from multiple sources, validation module that applies correlation rules and statistical analysis, and certification module that generates validation results. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while achieving high validation accuracy through coordinated operation of these segmented components
Solution Approach 2:
The validation system is designed with universal components that can handle multiple data types and validation scenarios. The correlation rule engine can process various combinations of data sources (sensor data, OBD data, historical data, external data) using the same fundamental validation algorithms. This multi-functional design allows the system to maintain high reliability across different validation scenarios without requiring separate specialized systems for each case
3Reliability
If real-time validation is performed, then data reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary validation actions by continuously collecting and pre-processing data from multiple sources in the background before validation is actually needed. Historical data patterns are pre-analyzed and stored, and correlation rules are pre-compiled. When real-time validation is required, the system leverages these pre-prepared elements to quickly verify new data against established patterns, significantly reducing actual validation processing time while maintaining comprehensive verification
Data Source
AI summary
The invention comprises a method and a system for validating data provided by an on-board diagnostics (OBD) system (10) of a vehicle (12). This method comprises receiving data related to the vehicle (10) (S1). The received data comprises a first data element (16) provided by the OBD system (10) of the vehicle (12) and at least one further data element (18). The first data element (16) and the further data element (18) are evaluated according to a correlation rule (22) (S2). This is done to provide a probability value that characterizes a probability with which the first data element (16) is manipulation-free. If the provided probability value (24) is in a specific probability value range (26), validation of the data provided by the OBD system (10) is confirmed (S3) by creating a certification signal (28) (S4). The created certification signal (28) is kept stored in a memory unit (30) (S5).