Dynamic Crash Classification for Supplemental Restraint Deployment
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Solution Overview
Problem
Existing vehicle supplemental restraint systems face challenges in accurately determining the severity of a crash event using multiple distributed acceleration sensors, as the relationship among acceleration signals can change during complex crashes and not all vehicles are equipped with the same set of sensors, necessitating a configurable and easily implemented deployment method.
Innovation Solution
A deployment method that dynamically classifies crash events based on relationships among acceleration signals, selects severity thresholds accordingly, and compares the crash severity measure to determine if restraints should be deployed, using a sensing and diagnostic module with multiple acceleration sensors to compute individual measures and select the highest severity indicator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple distributed acceleration sensors are used to improve crash severity measurement accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the vehicle into multiple sensing zones with distributed accelerometers (central, left-front, right-front, left-rear, right-rear) to independently measure acceleration in different regions. This segmentation allows accurate characterization of complex crash patterns while keeping each sensor unit simple and standardized.
Solution Approach 2:
The patent implements a universal processing algorithm that can handle data from various accelerometer configurations (3-sensor, 5-sensor, or other arrangements). The system universally applies crash pattern recognition and severity threshold comparison across different sensor setups, making the system adaptable without requiring complex custom processing for each configuration.
2Adaptability or versatility
If crash classification is made adaptive to different crash patterns, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically classifies crashes into different patterns (e.g., frontal, side, rear, angular) based on real-time comparison of acceleration signals from multiple sensors. The crash pattern classification and severity threshold selection adapt dynamically during the crash event based on the observed acceleration relationships, enabling responsive and accurate restraint deployment decisions.
Solution Approach 2:
The system changes processing parameters (severity thresholds, deployment criteria) based on the classified crash pattern. Different crash patterns have different severity thresholds and deployment rules stored in memory, allowing the system to adapt its response characteristics to match the specific crash type without requiring complex real-time calculations.
3Ease of operation
If severity thresholds are made time-independent and pre-calibrated, then ease of operation is improved, but measurement precision may be compromised
Solution Approach 1:
The system performs preliminary calibration during the design and testing phase to establish pre-calibrated severity thresholds for different crash patterns. These thresholds are stored in memory and selected based on the dynamically classified crash pattern, eliminating the need for complex real-time calibration while maintaining accuracy through pattern-specific pre-determined values.
Data Source
AI summary
A deployment method for a supplemental restraint system having multiple distributed acceleration sensors dynamically classifies each crash event based on relationships among the acceleration signals. A severity threshold is selected based on the instant crash classification, and a measure of crash severity is compared to the selected severity threshold to determine if the restraints should be deployed. The measure of crash severity is determined by computing individual measures of crash severity for the various frontal acceleration sensors and selecting the highest of the individual measures.