Vehicle Crash Sensor Normalization for Classification Accuracy
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Solution Overview
Problem
Existing methods for triggering passenger protection systems in vehicles do not effectively handle multiple sensor characteristics, leading to suboptimal crash event classification and inefficient resource utilization, particularly in machine-learning-based methods.
Innovation Solution
Mapping at least two sensor characteristics onto a single value range allows for equal weighting and flexible exchange, improving crash event classification and optimizing resource usage through normalization and efficient computation, enabling better performance and accuracy in passenger protection system activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor characteristics are processed with different value ranges, then more comprehensive crash information is obtained, but classification accuracy deteriorates due to unequal weighting and distorted statistical properties
Solution Approach 1:
The patent applies parameter changes by normalizing multiple sensor characteristics to a common value range (0 to 1). This transformation preserves the comprehensive information from multiple sensors while eliminating the distortion caused by different original value ranges, enabling equal weighting and accurate statistical property evaluation in machine learning algorithms.
2Productivity
If characteristics with different value ranges are used, then diverse sensor information is captured, but resource efficiency deteriorates due to increased computational overhead and storage requirements
Solution Approach 1:
The normalization process transforms characteristics with different value ranges into a unified 0 to 1 scale, reducing computational overhead by eliminating the need for range-specific processing logic and optimizing storage requirements through standardized data representation.
Solution Approach 2:
The patent creates homogeneity by converting all sensor characteristics to the same value range format. This uniform representation simplifies computational operations, enables consistent weighting across all characteristics, and optimizes resource utilization by eliminating the need to handle diverse data types and ranges separately.
3Measurement precision
If machine learning methods are applied to multiple characteristics, then classification performance improves, but resource consumption increases due to higher computational demands
Solution Approach 1:
By normalizing characteristics to a standard range before applying machine learning methods, the patent reduces computational complexity of the learning algorithms. The standardized input format enables more efficient computation of statistical properties and reduces the energy required for training and inference while maintaining or improving classification accuracy.
Data Source
AI summary
A method and a control device for triggering passenger protection means for a vehicle are provided, at least one sensor signal from an accident sensor system being provided by an interface. Furthermore, at least two characteristics are generated from the sensor signal, which are mapped onto one single value range. The triggering of the passenger protection means occurs as a function of the at least two mapped characteristics.