Fatigue Detection Model Using Sensor Data Weighting
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Solution Overview
Problem
Existing fatigue detection systems rely on biorhythm models that are not universally applicable, as they vary significantly between individuals and do not account for environmental and vehicle-specific factors, leading to ineffective attentiveness determination.
Innovation Solution
A model that utilizes sensors to gather data on vehicle brightness, driver operation, physiological states, and environmental conditions, such as temperature and geographical location, to weight fatigue determination, eliminating the need for a universal biorhythm model and incorporating vehicle-specific parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a universal biorhythm model is used for fatigue detection, then the system structure is simple, but the detection accuracy deteriorates because individual biorhythms vary significantly between drivers
Solution Approach 1:
The patent segments the fatigue detection approach by abandoning the universal biorhythm model and instead using multiple independent sensor systems (eye movement sensors, steering behavior sensors, physiological sensors) to capture individual driver characteristics. This segmentation allows the system to adapt to each driver's unique patterns without relying on a generalized model.
Solution Approach 2:
The system enables each driver to serve as their own reference by continuously monitoring their unique steering patterns, eye movements, and physiological responses. The fatigue detection is based on deviations from each driver's own baseline behavior rather than comparing against a universal biorhythm curve, making the system self-adapting to individual characteristics.
2Measurement precision
If environmental and vehicle-specific factors are incorporated into fatigue detection, then the detection accuracy improves, but the device complexity increases due to multiple sensors and parameters
Solution Approach 1:
The patent merges multiple sensor systems (brightness sensors, temperature sensors, GPS, steering sensors, eye movement sensors) into an integrated fatigue detection system. All these sensors work together to provide a comprehensive view of driver state and environmental conditions, with their data processed collectively to determine fatigue levels.
Solution Approach 2:
The system achieves multi-functionality by using a single integrated processing unit that handles data from various sensors (environmental, vehicle dynamics, driver behavior) to perform both environmental assessment and driver fatigue detection. This universal approach allows one system to serve multiple detection purposes without requiring separate dedicated systems for each function.
3Measurement precision
If individual driver characteristics are considered instead of universal biorhythms, then the detection accuracy improves, but the ease of operation deteriorates due to personalized calibration requirements
Solution Approach 1:
The system performs preliminary characterization of each driver's normal behavior patterns during an initial period before fatigue detection begins. By pre-establishing baseline steering patterns, eye movement characteristics, and physiological norms for each driver, the system eliminates the need for manual calibration during operation, making the personalized approach transparent to the user.
Solution Approach 2:
The system continuously monitors driver behavior and automatically adjusts the baseline profiles through feedback mechanisms. As the system collects more data on each driver's patterns, it refines the individualized models automatically, reducing the perceived complexity for the driver while maintaining high detection accuracy through adaptive learning.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a personalized and accurate assessment of driver attentiveness, enhancing fatigue detection by considering diverse factors, thereby improving safety through targeted alerts and warnings.
Implementation Method 1
A model (2) for determining the fatigue or attentiveness, or the degree of attentiveness, which is stored in a memory of a computing device (1) in a vehicle, is connected to a sensor (4) for determining the brightness in the vehicle vicinity
Implementation Method 2
The light sensor for determining the ambient brightness may be a function of the geographical location. That is to say, in a country such as Sweden, the brightness during the day is not as pronounced as in a country such as Spain. This influence is taken into account in that the information from a GPS or Galileo system is utilized and analyzed as well
Implementation Method 3
In addition, a sensor for measuring the temperature in the interior may be used and also considered in the analysis
Implementation Method 4
a state of the driver is detected with the aid of a sensor, such as a camera, and additionally utilized for fatigue evaluation. This activity may be determined as a function of eyelid movements or the pupil size
Data Source
AI summary
In a method for detecting driver fatigue in a vehicle having a memory device in which a fatigue model is stored, a sensor which detects at least one activity of the driver, and a sensor which detects the ambient brightness, the activity information is analyzed in the fatigue model and the brightness information is used for weighting the model.