Vehicle Rollover Detection Using Acceleration State Space
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Solution Overview
Problem
Existing vehicle rollover detection systems require multiple sensors and complex algorithms, making them costly and inefficient, particularly in determining crash severity and type, and often necessitate the use of roll rate sensors for accurate rollover detection.
Innovation Solution
A method utilizing only lateral and vertical acceleration sensor signals to determine the rollover position of a vehicle by interpreting these values in a state space, eliminating the need for roll rate sensors and allowing for precise detection of lateral and roof positions without additional equipment, and enabling the evaluation of post-crash situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors including roll rate sensors are used for accurate rollover detection, then detection reliability is improved, but system cost and complexity increase
Solution Approach 1:
The patent extracts and eliminates the roll rate sensor from the sensor system, achieving accurate rollover detection using only acceleration sensors. The method processes acceleration signals through characteristic curve separation and state space evaluation to detect lateral and roof positions without requiring rotational velocity data, thereby simplifying the sensor system while maintaining detection reliability
Solution Approach 2:
The acceleration sensors serve multiple functions: they provide data for both crash severity assessment and rollover position detection. The same acceleration signals used for general collision detection are reprocessed through state space analysis to determine lateral and roof positions, eliminating the need for dedicated roll rate sensors and reducing overall system complexity
2Measurement precision
If complex algorithms with multiple sensors are used for crash discrimination, then detection precision is improved, but system cost increases
Solution Approach 1:
The patent transitions from analyzing individual acceleration signals to evaluating the state space formed by combinations of lateral and vertical acceleration values. By mapping acceleration data into a two-dimensional state space with characteristic curves representing different crash types, the system achieves precise crash discrimination using the same sensor inputs, avoiding the need for additional sensors or more complex algorithms
Solution Approach 2:
The system pre-calculates and stores characteristic curves for different crash types in the state space before actual crash detection occurs. During operation, measured acceleration values are simply compared against these pre-established curves to determine crash type and severity, reducing real-time computational complexity while maintaining high precision in crash discrimination
Data Source
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AI summary
The present invention relates to a method (400) for detecting a lateral position of a vehicle and/or a position of a vehicle (100) when it lands on its roof, wherein the method (400) comprises a first step of receiving (410) a lateral value (130) and/or vertical value (150) via an interface, wherein the lateral value represents a lateral acceleration and/or the vertical value represents a vertical acceleration. The method (400) further comprises a second step of detecting (420) the position of the vehicle (100) when it lands on its roof, when a position value (200) derived from the vertical value (130) is in absolute terms greater in one component than a predefined vertical threshold value (230) and/or the method comprises a step of detecting the lateral position of the vehicle (100) when a position value (200) derived from the vertical value (130) and the lateral value (150) is in a lateral position region (300, 310, 320) of a state space, the state space being defined by axes (210, 220) in relation to a lateral and a vertical acceleration.