Sensor Arrangement Fallback Ranking for Vehicle Stability
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Solution Overview
Problem
Modern vehicles require highly reliable sensor architectures for driver assistance and safety, but existing systems fail to maintain functionality when individual sensors malfunction or provide inaccurate data, often necessitating vehicle repair and service intervention.
Innovation Solution
A method that ranks sensor data based on a stored evaluation metric, allowing fallback to alternative sensors that provide similar data, with parameters including accuracy, frequency of updates, and user trust values, enabling dynamic selection and use of the best available sensor data to maintain system stability and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor arrangements use multiple sensors for driver assistance and safety functions, then system reliability improves, but system complexity increases
Solution Approach 1:
The system pre-ranks sensors based on evaluation metrics before failures occur. This preliminary ranking allows the control unit to quickly switch to alternative sensors when failures happen, improving reliability without adding complex real-time decision-making logic during critical moments.
Solution Approach 2:
The system continuously monitors sensor status and dynamically adjusts rankings based on current performance data. This feedback mechanism ensures that the most reliable sensors are always selected, maintaining high system reliability while using a systematic approach that manages complexity.
2Stability of the object's composition
If the system implements real-time sensor ranking and fallback mechanisms, then system stability improves, but processing requirements and computational load increase
Solution Approach 1:
Sensors are ranked in advance using stored evaluation metrics before runtime failures occur. This preliminary ranking reduces the computational burden during critical failure moments, as the control unit simply needs to select from pre-ranked alternatives rather than performing complex real-time analysis.
Solution Approach 2:
The system dynamically adjusts sensor rankings based on current performance data while maintaining a balanced approach to computational load. The dynamic nature allows the system to adapt to changing conditions without requiring excessive processing power at any single moment.
3Reliability
If the system uses fallback sensors when primary sensors fail, then system availability improves, but data accuracy may vary across different sensor sources
Solution Approach 1:
The system uses evaluation metrics that incorporate data accuracy assessments to rank sensors. When selecting fallback sensors, the control unit chooses from ranked alternatives based on their proven accuracy performance, ensuring that even backup sensors meet quality standards.
Solution Approach 2:
The system changes the selection criteria for sensors based on their operational status. Primary sensors are selected based on normal operating parameters, while fallback selection uses different parameters including historical accuracy data and evaluation metrics, allowing the system to maintain data quality across different operational states.
Data Source
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AI summary
The present invention is directed towards a method for improving stability of sensor arrangements and a respective control device along with a computer program. The present invention allows a robust operation of sensor arrangements as upon failures of single sensors further sensors providing similar data are addressed.