Vehicle Occupant Classification Using Seated Height and Weight Sensors
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Solution Overview
Problem
Existing occupant detection and classification systems in vehicles rely on user-provided height and weight, which are unreliable as they can change over time and are often unavailable, making it difficult to accurately classify occupants for tuning vehicle subsystems.
Innovation Solution
A vehicle classification system that determines an occupant's seated body mass index (BMI) using seated height and weight measurements, excluding the weight of the legs, and adjusts for seat angle, allowing classification without user input, using sensors and a processor to assign classifications like underweight, normal weight, or obese, and tune vehicle subsystems accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user-provided height and weight are used for occupant classification, then the system is simple to implement, but the reliability of classification is poor because user-provided data can change over time and are often unavailable
Solution Approach 1:
The system performs self-measurement of occupant height and weight using onboard sensors (weight sensor in the seat, distance sensor measuring vertical distance from seat to occupant's head). The vehicle system automatically obtains the data without requiring user input, making the system self-sufficient and reliable.
Solution Approach 2:
The patent replaces manual user input with automated sensor-based measurement systems. Weight sensors and distance sensors (optical or other types) automatically measure occupant parameters, substituting the mechanical/manual process of user self-reporting with electronic sensing and processing.
2Measurement precision
If seated weight measurement excluding legs is used, then the measurement precision of occupant mass is improved, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The weight measurement is segmented into two parts: seated weight (measured by the weight sensor, excluding legs) and leg weight (calculated as the difference between standing weight from user profile and seated weight). This segmentation allows the system to obtain more accurate trunk and upper body mass measurements while still utilizing existing user profile data.
Solution Approach 2:
The system uses the user's standing weight from their profile as an intermediary reference point. By comparing this known standing weight with the measured seated weight, the system can calculate the weight of the legs and derive the seated weight of the trunk and upper body, achieving precise measurement without directly measuring each body segment.
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
Enables accurate classification of vehicle occupants without requiring them to provide their height and weight, ensuring reliable tuning of vehicle subsystems such as restraint systems, improving safety and comfort by adjusting settings like airbag deployment and seat positioning based on occupant size.
Implementation Method 1
a weight sensor to measure the occupant's seated weight
Implementation Method 2
a distance sensor to measure a vertical distance from the seat to a top of the occupant's head
Data Source
AI summary
A vehicle classification system includes a processor programmed to determine a seated height and seated weight associated with a vehicle occupant. The processor is further programmed to assign a classification to the vehicle occupant based at least in part on a ratio of the seated weight to the seated height.