Driver Comfort Index via Physiological and Behavioral Sensor Fusion
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Solution Overview
Problem
Current driver comfort measurement systems primarily focus on individual mental states like fatigue or drowsiness, neglecting the overall experience of discomfort during driving and lacking objective behavioral and physiological data analysis.
Innovation Solution
A control device that integrates physiological and behavioral sensors with a driving context detection unit to create a reference data set, determining a comfort level index by considering current sensor outputs, driving context, and historical data, allowing for real-time discomfort detection and anticipation of potential negative impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual mental states (fatigue, drowsiness) are measured separately using behavioral or physiological parameters, then specific driver states can be detected, but the overall discomfort experience during driving cannot be comprehensively assessed
Solution Approach 1:
The patent combines multiple individual mental state measurements (fatigue, drowsiness, stress, vigilance) into a unified discomfort assessment system. By integrating data from physiological sensors (heart rate, skin conductance, muscle activity) and behavioral sensors (eye tracking, steering patterns, pedal usage), the system creates a comprehensive discomfort index that reflects the driver's overall comfort experience rather than isolated mental states.
Solution Approach 2:
The control device is designed to perform multiple functions: it detects specific mental states individually while simultaneously assessing overall discomfort levels. The system adapts to different driving contexts and can evaluate various types of discomfort (physical, mental, emotional) through a single integrated platform that processes diverse sensor inputs and generates comprehensive comfort assessments.
2Loss of information
If subjective data from questionnaires is used to analyze driver discomfort, then driver self-perception can be captured, but objective behavioral and physiological data analysis is neglected
Solution Approach 1:
The system incorporates feedback loops where objective sensor data (physiological and behavioral measurements) continuously monitors and validates subjective driver reports. The control device compares questionnaire responses with actual sensor-derived comfort indices, allowing it to refine its assessments and provide more accurate objective measurements while still valuing driver self-perception as a complementary information source.
Solution Approach 2:
The control device acts as an intermediary that bridges subjective driver experiences and objective measurements. It processes both questionnaire data and sensor outputs, reconciling them into a unified discomfort assessment that captures both the driver's perceived comfort level and the objectively measured physiological and behavioral indicators of comfort or discomfort.
3Productivity
If only current driving situation data is analyzed, then immediate safety risks can be detected, but long-term discomfort effects and trends cannot be anticipated
Solution Approach 1:
The system performs preliminary actions by continuously accumulating and analyzing historical comfort data alongside current measurements. The control device maintains a running record of driver comfort levels across different driving contexts, allowing it to detect emerging discomfort trends before they lead to significant safety issues. This proactive approach enables early intervention while still providing real-time feedback for immediate safety concerns.
Solution Approach 2:
The discomfort assessment system dynamically adapts its analysis window, switching between short-term real-time monitoring for immediate safety risks and long-term trend analysis for anticipating chronic discomfort effects. The control device adjusts the weight given to historical versus current data based on the driving context and detected patterns, providing both immediate detection capabilities and long-term predictive insights.
4Measurement precision
If comprehensive physiological and behavioral sensors are integrated with driving context detection, then accurate discomfort measurement is achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex sensor system into distinct functional modules: physiological sensing subsystem, behavioral sensing subsystem, driving context detection unit, and comfort calculation unit. Each module independently processes specific types of data and communicates through standardized interfaces, reducing overall system complexity while maintaining comprehensive measurement capabilities. This modular architecture allows for easier maintenance, calibration, and adaptation of individual sensor components.
Data Source
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AI summary
The invention relates to a control device (1) for a vehicle for a vehicle (10) for determining a comfort level of a driver, the control device (1) being configured to: • receive sensor output of a physiological sensor (2) and a behavioral sensor (3), the physiological sensor (2) measuring at least one physiological feature of the driver and the behavioral sensor (3) measuring at least one behavioral feature of the driver, • receive driving context information from a driving context detection unit (4), • create a reference data set by recording the sensor output and the driving context information over a predetermined reference time period, • determine a reference index for the comfort level of the driver based on the reference data set, and • determine a comfort level index value of the driver, the index value being determined as a function of a current sensor output, current driving context information and the reference index. The invention further relates to a system and a method.