Crash Event Discrimination Using Multi-Axis Satellite Sensors
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Solution Overview
Problem
Existing vehicle occupant restraining systems struggle to accurately discriminate between different types of vehicle crash events, particularly high speed frontal rigid barrier impacts, offset deformable barrier impacts, oblique/angular frontal rigid barrier impacts, and small/narrow overlap impacts, leading to inadequate deployment decisions and timing in modern crashworthiness evaluations.
Innovation Solution
The use of remote side multi-axis satellite sensors and crush zone multi-axis sensors, combined with a unique evaluation process and crash classification arrangement, allows for enhanced discrimination of these events, adjusting the deployment control algorithm for quicker actuation of restraining devices, such as airbags and seatbelt pretensioners, and improved control of seat-belt load limiters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional accelerometer-based crash sensors are used to determine deployment events, then the system can detect crash acceleration, but it cannot accurately discriminate between different types of crash events (deployment vs. non-deployment)
Solution Approach 1:
The patent divides the vehicle into multiple sensing zones by placing accelerometers in different locations: a first accelerometer in the crush zone and a second accelerometer remote from the crush zone. This segmentation allows the system to analyze acceleration patterns from different regions to better discriminate between crash types.
Solution Approach 2:
The patent adds spatial dimension to crash detection by using multi-axis accelerometers that measure acceleration in multiple directions (X, Y, Z axes). This dimensional expansion enables the system to distinguish between different crash vectors and types, such as frontal vs. side impacts, improving discrimination accuracy.
2Reliability
If the restraining system is actuated for all detected crash events, then occupant safety is maximized, but false deployment occurs for non-deployment events (e.g., undercarriage snag)
Solution Approach 1:
The system uses feedback from multiple accelerometer signals to continuously monitor and evaluate crash patterns. By comparing acceleration magnitudes, directions, and temporal patterns from different sensor locations, the system provides feedback to the deployment decision logic to distinguish true crash events from false triggers.
Solution Approach 2:
The patent changes deployment thresholds and evaluation parameters based on the specific crash pattern detected. Different acceleration patterns trigger different deployment criteria, allowing the system to adjust its response based on the severity and type of event, reducing false deployments while maintaining safety for true crashes.
3Measurement precision
If multiple sensors and complex discrimination algorithms are added to improve crash event discrimination, then deployment accuracy improves, but system complexity increases
Solution Approach 1:
The patent uses multi-axis accelerometers that serve multiple functions: detecting crash acceleration magnitude, determining crash direction, identifying crash type, and triggering appropriate deployment responses. This multi-functionality reduces the need for separate sensors for each detection purpose, managing system complexity.
Solution Approach 2:
The patent replaces complex mechanical sensor switches with electronic accelerometer-based detection and software-based discrimination algorithms. This substitution simplifies the physical hardware while enabling more sophisticated crash analysis through electronic signal processing and computational logic.
4Object-generated harmful factors
If the deployment algorithm uses conservative thresholds to avoid false deployment, then false deployment is reduced, but response time to actual crashes is delayed
Solution Approach 1:
The patent implements dynamic threshold adjustment where deployment criteria change based on the detected acceleration pattern. For high-severity crash patterns, lower thresholds and faster response are enabled, while for ambiguous patterns, higher thresholds apply. This dynamic adaptation optimizes both response time and false deployment reduction.
Solution Approach 2:
The system performs preliminary analysis of acceleration patterns from multiple sensors before making deployment decisions. By pre-evaluating crash vectors, magnitudes, and patterns against stored crash profiles, the system prepares deployment parameters in advance, enabling faster response when true crashes are detected while maintaining conservative thresholds for ambiguous events.
Data Source
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AI summary
A method for controlling an actuatable restraining device includes sensing a plurality of crash event indications, classifying crash events in response to the sensed crash event indications to identify at least one of a forward rigid barrier crash event, an offset deformable barrier crash event, an angular crash event, and a small overlap crash event, and controlling deployment timing of the actuatable restraining device in response to the classification of the crash event.