Vehicle Safety Grading Using Statistical VOSM Parameter Baselines
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Solution Overview
Problem
Current vehicle operation safety models (VOSMs) provide only binary safe or not safe results, which are insufficient for complex use cases and do not account for differences in driving strategies that may impact long-term safety and costs.
Innovation Solution
A system for measuring VOSM safety grades using statistical parameter analysis, which collects vehicle datasets, calculates statistical values for VOSM parameters across various modes, and compares these values to determine a safety grade for individual vehicles, allowing for differentiation in safety levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary safe/not safe results are used in VOSM, then the evaluation process is simple, but the assessment precision is insufficient for complex use cases
Solution Approach 1:
The patent segments the binary safety assessment into multiple graded levels (e.g., safe, partially safe, unsafe) based on statistical parameter analysis. This segmentation allows for more precise differentiation of safety states while maintaining a structured evaluation framework that can be implemented through systematic comparison of vehicle operation parameters against statistical thresholds.
Solution Approach 2:
The patent changes the parameter representation from a single binary outcome to multiple continuous parameters including statistical values (mean, standard deviation, percentiles) of safety metrics. This parameter transformation enables nuanced safety assessment by analyzing distributions rather than single points, improving precision while the parameter changes are managed through automated computational processes.
2Measurement precision
If statistical parameter analysis is implemented for safety grading, then the assessment precision improves, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary statistical analysis by pre-calculating and storing statistical parameters (mean, standard deviation, percentiles) from historical vehicle operation data. This preliminary action creates a reference database that enables rapid safety grading during actual evaluation without requiring complex real-time calculations, thus improving precision while managing computational complexity through offline preparation.
Solution Approach 2:
The patent creates simplified copies of complex statistical distributions by representing them through key statistical parameters (mean, standard deviation, percentiles) rather than full distribution functions. This copying approach allows for efficient comparison and grading by working with condensed statistical representations that capture essential safety characteristics without requiring computationally intensive operations.
3Loss of information
If detailed safety grades are assigned to differentiate driving strategies, then the information value increases, but the data processing requirements increase
Solution Approach 1:
The patent extracts key statistical parameters (mean, standard deviation, percentiles) from large volumes of raw vehicle operation data, separating the essential safety information from the full dataset. This extraction process retains the critical information needed for safety grading while reducing data volume, allowing detailed safety assessment without proportionally increasing data processing requirements.
Solution Approach 2:
The patent inverts the traditional approach by first establishing statistical baselines from population data and then comparing individual vehicle performances against these baselines to generate safety grades. This inversion allows the system to leverage pre-computed statistical references, reducing the processing burden on individual vehicle evaluations while still providing detailed and differentiated safety information.
Data Source
AI summary
System and techniques for vehicle operation safety model (VOSM) grade measurement are described herein. A data set of parameter measurements—defined by the VOSM—of multiple vehicles are obtained. A statistical value is then derived from a portion of the parameter measurements. A measurement from a subject vehicle is obtained that corresponds to the portion of the parameter measurements from which the statistical value was derived. The measurement is then compared to the statistical value to produce a safety grade for the subject vehicle.