Vehicle Safety Feature Effectiveness Scoring Across OEM Terminology
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Solution Overview
Problem
It is challenging to determine the effectiveness of smart safety features in vehicles, as existing methods struggle to compare the safety performance across different manufacturers due to varying 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 a common language using an ontology model, and calculate effectiveness scores based on accident data, including feature usage and software versions, to assess the performance of smart safety features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle build information from multiple OEMs is collected and analyzed using ontology models to translate OEM-specific terminology into common language, then the ability to compare and rank smart safety features across different manufacturers is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
An ontology model serves as an intermediary layer between OEM-specific terminology and common language. The ontology model maps and translates proprietary terms from different manufacturers into standardized safety feature categories, enabling accurate comparison without requiring direct integration with each OEM's proprietary systems.
Solution Approach 2:
The system creates a universal data structure that can handle multiple OEM-specific formats simultaneously. By defining common language categories for safety features that map to various manufacturer-specific implementations, the system achieves multi-functional capability to process diverse data sources through a single standardized interface.
2Reliability
If comprehensive vehicle build information and accident data are collected and analyzed, then the reliability of safety feature effectiveness assessment is improved, but the loss of time and computational resources increases
Solution Approach 1:
Ontology models and translation mappings are pre-established before actual effectiveness assessment. The system pre-defines the relationships between OEM-specific terms and common language categories, so that during data collection and analysis, the translation process occurs efficiently without requiring complex real-time processing.
Solution Approach 2:
The system creates standardized copies of safety feature data from various OEM formats. By translating and normalizing proprietary data into common language representations, the system works with simplified copies rather than processing the full complexity of original diverse formats, reducing computational overhead.
3Measurement precision
If detailed operational data including feature usage and software versions are tracked for each vehicle, then the precision of effectiveness measurement is improved, but the quantity of data to be managed and processed increases
Solution Approach 1:
The system applies different levels of data collection and processing granularity to different safety features based on their specific characteristics. Rather than uniformly processing all data at maximum detail, the ontology model allows selective depth of analysis tailored to each feature type, reducing overall data volume while maintaining necessary precision for each specific assessment.
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.