Vehicle Safety Feature Scoring Across OEM Terminology Gaps
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Solution Overview
Problem
It is challenging to determine the effectiveness of vehicle safety features, as existing methods struggle to compare the performance of different safety features across various vehicle manufacturers due to OEM-specific terminology and lack of data on feature usage during accidents.
Innovation Solution
A system and method that collect and analyze vehicle build information, translate OEM-specific terminology into OEM-agnostic terms using an ontology model, and calculate effectiveness scores for safety features based on accident data and telematics information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle safety features are equipped with smart safety features, then vehicle safety is improved, but it becomes difficult to determine which features are most effective due to OEM-specific terminology
Solution Approach 1:
The patent introduces an intermediary translation layer that converts OEM-specific safety feature terminology into standardized terminology. This mediator enables consistent identification and comparison of safety features across different manufacturers, resolving the measurement precision problem while preserving the reliability improvement from smart safety features.
Solution Approach 2:
The patent creates a universal translation framework that handles multiple OEM terminologies through a single standardized system. This universal approach allows the same safety feature to be identified regardless of which manufacturer's terminology is used, enabling effective determination of feature performance across the entire vehicle fleet.
2Quantity of substance
If vehicle build information is collected for multiple OEMs, then comprehensive safety analysis is enabled, but data processing complexity increases due to OEM-specific terminology
Solution Approach 1:
The translation system acts as an intermediary processing layer that simplifies complex multi-OEM data into a unified format. Rather than creating separate processing pipelines for each OEM, the mediator translates all incoming data through a single standardized interface, reducing overall system complexity while maintaining comprehensive data coverage.
Solution Approach 2:
The patent transforms the terminology parameter from its original OEM-specific state into a standardized form. This parameter change converts diverse input formats into a uniform structure, enabling simplified processing of comprehensive vehicle data across all manufacturers without increasing system complexity.
3Measurement precision
If effectiveness scores are calculated for each safety feature, then safety performance comparison is enabled, but tracking effectiveness of feature updates becomes difficult
Solution Approach 1:
The patent implements a feedback mechanism where effectiveness scores are continuously calculated and stored in a centralized repository. This feedback loop enables automatic tracking of how safety feature updates impact effectiveness scores over time, allowing stakeholders to monitor improvements without manual intervention and preventing information loss.
4Adaptability or versatility
If standardized terminology is implemented across OEMs, then data comparability is improved, but initial data translation effort and system complexity increase
Solution Approach 1:
The translation system serves as a one-time intermediary setup that establishes standardized terminology mappings. Once this mediator is configured, it enables ongoing data comparability across all OEMs without requiring continuous complex processing, as the translation framework becomes a reusable infrastructure component.
Data Source
AI summary
The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may 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.