Vehicle Feature Update Ledger for Cross-OEM Safety Assessment
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Solution Overview
Problem
It is difficult to determine the effectiveness of vehicle safety features in preventing accidents and to track the effectiveness of feature updates due to variability in OEM-specific terminology and the lack of information on feature usage during accidents.
Innovation Solution
An ontology model is generated to translate OEM-specific terminology into OEM-agnostic terminology, and vehicle build information is analyzed with accident records to calculate effectiveness scores for smart safety features, incorporating telematics data to assess feature performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vehicle build information is collected from multiple OEMs with different terminology, then the quantity of data available for analysis increases, but the difficulty of analyzing and comparing data increases due to OEM-specific terminology variations
Solution Approach 1:
The patent introduces an intermediary translation layer that converts OEM-specific terminology into a standardized language. This intermediary system includes a terminology mapping database and translation engine that acts as a mediator between diverse OEM data sources and the analysis system, enabling uniform processing of vehicle build information, accident records, and telematics data from multiple manufacturers without requiring complex custom analysis for each OEM's terminology
Solution Approach 2:
The patent creates a universal data analysis platform that can process vehicle safety feature data from multiple OEMs simultaneously. The system uses standardized terminology and unified analysis methods that work across different manufacturers, making the system multi-functional rather than requiring separate analysis systems for each OEM. This universal approach enables comprehensive cross-OEM comparisons while maintaining data consistency
2Measurement precision
If effectiveness scores are calculated for smart safety features, then the precision of safety assessment improves, but the difficulty of tracking updates and maintaining information increases
Solution Approach 1:
The patent implements a feedback mechanism where effectiveness scores are continuously calculated and fed back into the system to update the distributed ledger. When new accident data or telematics data becomes available, the system recalculates effectiveness scores and updates the ledger accordingly. This feedback loop ensures that the most current effectiveness information is maintained and accessible, preventing information loss while providing precise, up-to-date safety assessments
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing effectiveness scores in the distributed ledger before they are needed for analysis. The system proactively updates the ledger with new effectiveness data as it becomes available, rather than waiting for requests. This preliminary preparation ensures that effectiveness information is always current and readily accessible, eliminating the need for repeated calculations and preventing information loss
3Reliability
If telematics data and accident records are integrated for analysis, then the reliability of effectiveness determination improves, but the device complexity increases
Solution Approach 1:
The patent segments the complex data integration process into distinct modular components: a vehicle build information module, an accident record module, a telematics data module, and an effectiveness calculation module. Each module handles specific data types and processing tasks independently. The distributed ledger serves as a centralized coordination layer that integrates these segments. This segmentation reduces complexity by allowing each component to be developed, maintained, and validated independently while still contributing to the overall reliable effectiveness determination
Data Source
AI summary
The following relates generally to tracking effectiveness of an update to a vehicle feature using a distributed ledger. In some embodiments, a distributed ledger including a vehicle feature is added to or constructed. Information indicating an update to the vehicle feature, and accident record information may then be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets, and added to the distributed ledger.