Fatigue Evaluation System Using Metadata Weighting
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Solution Overview
Problem
Current methods for evaluating a driver's fatigue in vehicles are not sufficiently robust and flexible, often leading to inaccurate assessments, especially when driver assistance systems are used, as they may not adequately account for situational awareness and physiological parameters.
Innovation Solution
A method that combines multiple fatigue indicators from vehicle sensors and physiological parameters, using metadata records to weight and combine sensor values for a more accurate evaluation, incorporating both vehicle-related and occupant-related data to improve the reliability of fatigue assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple fatigue indicators from vehicle sensors and physiological parameters are combined using metadata records for weighting, then the accuracy and robustness of fatigue evaluation is improved, but the device complexity increases
Solution Approach 1:
The fatigue evaluation system is segmented into multiple independent indicator modules, each processing specific sensor data or physiological parameters. Metadata records are segmented to store weighting information for each indicator separately. This segmentation allows the system to manage complexity through modular organization while maintaining high evaluation accuracy through comprehensive multi-indicator assessment.
Solution Approach 2:
The metadata record structure serves multiple functions: it stores weighting factors for different fatigue indicators, manages sensor value characteristics, and enables adaptive evaluation across various driving scenarios. This multi-functional design allows a single data structure to support complex evaluation logic without proportionally increasing system complexity.
2Device complexity
If traditional fatigue monitoring methods are used based on steering behavior and driving time, then the system simplicity is maintained, but the reliability of fatigue assessment deteriorates
Solution Approach 1:
The system merges traditional fatigue indicators (steering behavior, driving time) with physiological parameters (heart rate, breathing rate) and vehicle sensor data. This combination integrates multiple data sources into a unified evaluation framework, significantly improving reliability while maintaining reasonable system simplicity through the use of standardized metadata records for data management.
Solution Approach 2:
The system changes from monitoring a limited set of traditional parameters to evaluating multiple fatigue indicators with dynamically adjustable weighting factors stored in metadata records. This parameter expansion and flexibility allows the system to adapt to different driving scenarios and driver states, improving reliability without requiring complete system redesign.
3Measurement precision
If video camera-based lid closure time monitoring is used, then the measurement precision of fatigue detection is improved, but the adaptability to different driving situations deteriorates
Solution Approach 1:
The system implements dynamic adaptability by storing weighting factors in metadata records that can be adjusted based on driving situations, driver profiles, and environmental conditions. This dynamic weighting allows the system to adapt the contribution of lid closure time and other indicators to different scenarios, maintaining high measurement precision while achieving situational versatility.
Solution Approach 2:
The system changes the evaluation parameters by introducing metadata records that store and manage weighting factors for different fatigue indicators. This allows flexible parameter adjustment where lid closure time monitoring can be weighted higher in certain situations while other indicators gain prominence in different contexts, achieving both precision and adaptability.
Data Source
AI summary
A method evaluates a degree of fatigue of a vehicle occupant in a vehicle. A number of first fatigue indicators is provided which are determined according to computation rules from a plurality of first sensor values and each represent a degree of fatigue of the vehicle occupant. The first sensor values represent measured values of the vehicle and/or measured values relating to a current journey. A first metadata record is associated with each of the number of first fatigue indicators, wherein the first metadata records represent information about the characteristics of the sensors. The first sensor values are processed in the respective first fatigue indicators. A number of second fatigue indicators is provided which are determined according to computation rules from one or more second sensor values and each represent a degree of fatigue of the vehicle occupant. The second sensor values represent physiological and/or physical parameters of the vehicle occupants. A second metadata record is associated with each of the number of second fatigue indicators. The second metadata records represent information about the characteristics of the sensors. The second sensor values are processed in the respective second fatigue indicators. An overall fatigue indicator is determined which represents the degree of fatigue of the vehicle occupant by weighting the number of first fatigue indicators and the number of second fatigue indicators. The fatigue indicators are weighted according to the information about the characteristics of the sensors contained in the first metadata record and the second metadata record.