Vehicle User Profile Differentiation via Feature Relevance Scoring
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Solution Overview
Problem
Vehicles struggle to accurately differentiate between multiple users, often creating a single user profile due to inconsistent feature usage, leading to incorrect settings application and learning.
Innovation Solution
A system utilizing a processor and memory to store modeling parameters generated by an algorithm that assigns relevance scores to vehicle features, allowing for the identification of the current user based on configuration data, enabling the application of the correct user profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the vehicle learns from all users together as a single profile, then the learning process is simple, but the accuracy of user identification and settings application deteriorates
Solution Approach 1:
The patent segments users into different profiles by analyzing configuration data patterns. The system divides the single learned profile into multiple distinct user profiles based on detected usage patterns, allowing accurate identification of individual users while maintaining manageable complexity through automated pattern recognition.
Solution Approach 2:
The system changes parameters by introducing confidence scores and pattern matching thresholds. When configuration data matches an existing profile with sufficient confidence, that profile is applied; otherwise, the system may create or switch to alternative profiles, dynamically adjusting identification accuracy based on parameter thresholds.
2Ease of operation
If the vehicle uses a single user profile for all users, then the system is easy to operate, but the adaptability to individual user preferences deteriorates
Solution Approach 1:
The system provides self-service by automatically detecting user preferences through configuration data and applying appropriate profiles without requiring manual user input. The vehicle learns and adapts to individual user preferences autonomously, maintaining ease of operation while achieving high adaptability through automated pattern recognition and profile selection.
Solution Approach 2:
The system dynamically adapts between single-profile and multi-profile modes based on detected usage patterns. When consistent patterns emerge indicating multiple users, the system transitions to dynamic profile switching, automatically adapting to individual preferences while maintaining simple operation through automated detection and selection.
3Measurement precision
If the vehicle monitors and analyzes configuration data to differentiate users, then user identification accuracy is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential configuration data elements needed for user differentiation, such as seat positions, mirror adjustments, and climate preferences. By focusing on extracting and analyzing only the most discriminative features rather than processing all vehicle data, the system achieves accurate user classification while managing computational complexity.
Data Source
AI summary
A system for a vehicle includes a memory configured to store modeling parameters, generated using an algorithm that assigns relevance scores to features, that specify information indicative of how configuration data including information indicative of use of features of the vehicle classifies which user is currently using the vehicle. The system also includes a processor programmed to monitor the vehicle for the configuration data, identify a most likely user profile using the modeling parameters according to the configuration data, and apply settings of the user profile to the vehicle.