Vehicle HMI Personalization for Multi-Occupant Preference Merging
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Solution Overview
Problem
Existing vehicle human machine interfaces (HMIs) lack the ability to dynamically adapt and customize settings based on the identities and preferences of multiple occupants, leading to suboptimal user experience and interaction efficiency.
Innovation Solution
A system and method that determines the identities of vehicle occupants using biometric identification, retrieves their interface settings, and performs machine learning to generate a personalized HMI by merging these settings, considering factors like seating, roles, and relationships among occupants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustments are made to customize HMI settings for each occupant, then user preference alignment is improved, but operation time and complexity increase
Solution Approach 1:
The system performs preliminary actions by automatically identifying occupants and applying their preferred HMI settings without requiring manual adjustment. The computation device determines occupant identities, retrieves stored interface settings, and applies them automatically, eliminating the time-consuming manual customization process while maintaining high adaptability to user preferences
Solution Approach 2:
The system enables self-service by automatically detecting occupant presence and applying appropriate HMI settings without human intervention. The occupation determination device and biometric identification device work together to identify occupants, and the computation device automatically configures the interface based on stored preferences, allowing the system to serve itself rather than requiring manual user configuration
2Adaptability or versatility
If HMI settings are customized for multiple occupants, then user experience quality is improved, but system complexity increases
Solution Approach 1:
The system segments the HMI customization process into distinct functional modules: occupation determination device for detecting occupant presence, biometric identification device for recognizing specific occupants, database for storing interface settings, and computation device for processing and applying settings. This segmentation manages complexity by dividing the overall system into specialized, manageable components while enabling comprehensive multi-occupant customization
Solution Approach 2:
The computation device serves multiple functions by integrating occupation determination, biometric identification, settings retrieval, and HMI configuration operations. This multi-functionality reduces overall system complexity by consolidating multiple operations into a single versatile device while maintaining the capability to customize interfaces for multiple occupants
Data Source
AI summary
A method for generating a vehicle human machine interface is disclosed. The identities of each of a plurality of occupants in a vehicle are determined. A plurality of interface settings corresponding to the plurality of occupants are obtained according to the identities. A machine learning operation is performed according to the identities of the plurality of occupants and the plurality of interface settings. The vehicle human machine interface is generated according to a result of the machine learning operation.