In-Cabin Occupant Age Grouping From Movement Range and Timing
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Solution Overview
Problem
Existing systems lack the ability to accurately determine the age of vehicle occupants based on their movements within the vehicle cabin, which is crucial for personalized services and safety assessments.
Innovation Solution
A method and system that utilize vehicle sensors to record and analyze the maximum range of extension and time between movements of an occupant's extremities to assign them to age groups, leveraging machine learning and blockchain technology for secure data management and authorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle sensors record and analyze occupant movements to determine age, then age determination accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the age determination process into distinct analytical components: extracting movement parameters (range of extension, time between movements), classifying movements into types, and determining age groups based on aggregated data. This segmentation simplifies the overall system by breaking down the complex task of age determination into manageable, modular steps that can be processed independently.
Solution Approach 2:
The system transforms physical movement characteristics into measurable parameters suitable for age determination. By converting raw sensor data into specific parameters such as maximum range of extension, time between movements, and movement velocity, the system enables accurate age assessment while maintaining computational efficiency and reducing system complexity.
2Measurement precision
If multiple movement parameters are analyzed to improve age group classification, then classification accuracy is improved, but data processing time increases
Solution Approach 1:
The system extracts only the most relevant movement parameters from the complete sensor data set, specifically focusing on maximum range of extension, time between movements, and movement velocity. By selectively extracting these key parameters rather than processing all available data, the system achieves accurate age group classification while minimizing data processing time and computational resources required.
Solution Approach 2:
The system analyzes a sufficient subset of movement parameters needed for accurate age determination without processing every possible movement characteristic. By identifying and analyzing only the critical parameters that contribute most significantly to age group classification, the system achieves high accuracy while avoiding the time cost of exhaustive analysis of all movement data.
Data Source
AI summary
An example operation includes recording, from a vehicle sensor, data indicative of a plurality of movements of a first occupant in an interior of a vehicle; determining, from the recording, a first maximum range of extension among the plurality of movements, and a first amount of time between a first movement and a second movement of the plurality of movements; and assigning the first occupant to one of a plurality of age groups, based upon the determining.