Vehicle Occupant Profile Linking for Accurate Auto Configuration
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Solution Overview
Problem
Existing vehicle systems face performance degradation and inefficiency due to outdated or inaccurate user profiles, leading to sub-optimal operation and potential system failures, especially when vehicle components change or operating conditions differ from previous usage.
Innovation Solution
A vehicle generates and updates occupant profiles using facial image data and machine-learning models, linking local profiles with cloud-based profiles to receive configuration data for optimal operation, including personalized settings and system updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle systems use stored user profiles for customization, then user preferences can be maintained, but the profiles become outdated and inaccurate when vehicle components change or operating conditions differ
Solution Approach 1:
The system continuously monitors vehicle operating conditions, component states, and user interactions to detect when profile data becomes outdated. This feedback mechanism triggers automatic profile updates, ensuring the profile remains accurate without requiring manual intervention or waiting for scheduled updates.
Solution Approach 2:
The system proactively updates user profiles by detecting changes in vehicle components and operating conditions before they significantly impact performance. By performing preliminary detection and update actions, the system prevents profile obsolescence rather than reacting to it after problems occur.
2Reliability
If vehicle systems continuously update profiles to maintain accuracy, then customization remains optimal, but system complexity and computational resources increase
Solution Approach 1:
The vehicle system automatically performs profile updates by detecting changes in its own components and operating conditions. The system serves itself by monitoring its state and autonomously refreshing profile data without requiring external intervention or complex centralized management infrastructure.
Solution Approach 2:
The system uses feedback from vehicle sensors and component status to automatically determine when profile updates are needed. This feedback-driven approach simplifies the management system by using existing vehicle data streams rather than requiring separate complex monitoring and update mechanisms.
3Productivity
If vehicle systems use outdated user profiles, then system operation becomes sub-optimal and resource waste increases, but updating profiles requires additional processing and communication
Solution Approach 1:
The vehicle system automatically detects when its components or operating conditions change and self-initiates profile updates using existing sensor data and communication protocols. This self-service approach minimizes additional processing requirements by leveraging already-collected vehicle data rather than requiring separate comprehensive scanning procedures.
Solution Approach 2:
The system updates profile parameters dynamically based on detected changes in vehicle state, only refreshing the specific data points that have changed rather than performing complete profile re-synchronization. This selective parameter updating reduces communication and processing energy consumption while maintaining profile accuracy.
Data Source
AI summary
A profile is automatically generated for an occupant of a vehicle. In one approach, data is collected from an interior of a vehicle to determine whether an occupant is present. If an occupant is present, a local profile is automatically generated. The local profile is sent to a remote computing device. The remote computing device links the local profile to a remote profile stored by the remote computing device. Configuration data is generated by the remote computing device based on linking the local and remote profiles. The configuration data is sent to the vehicle and used by the vehicle to control the operation of one or more components of the vehicle.