Vehicle Digital Twin Personalization Using Satisfaction-State Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital twin systems for passenger vehicles are limited in managing and enhancing customer experience, with primitive interfaces that do not offer configuration options based on user profiles, and lack advanced features to optimize vehicle operations and user satisfaction.
Innovation Solution
A configurable digital twin interface system that uses neural networks to detect and respond to user satisfaction states, optimizing vehicle parameters such as route, audio, speed, and proximity to enhance user experience, integrated with identity management and edge intelligence for personalized interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital twin systems use primitive interfaces without configuration options, then device complexity is reduced, but user satisfaction and adaptability deteriorate
Solution Approach 1:
The digital twin interface dynamically adapts its configuration based on user profiles and real-time vehicle state data. The system automatically adjusts interface parameters such as displayed metrics, alert thresholds, and visualization styles according to the logged-in user's preferences and role, transforming a static primitive interface into a dynamic personalized experience without requiring complex manual configuration options
Solution Approach 2:
The system employs machine learning models that automatically analyze user behavior patterns and vehicle operational data to self-configure the interface. The digital twin learns from user interactions and autonomously optimizes interface presentation, eliminating the need for users to manually configure complex interface settings while maintaining high adaptability
2Ease of operation
If digital twin systems lack user profiling capabilities, then device complexity is reduced, but ease of operation and user experience deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically creating and maintaining user profiles in the background before users need personalized features. Identity management services pre-process user authentication data and vehicle state information to prepare personalized interface configurations, so when users log in, their personalized experience is already ready without requiring them to manually set up profiles
Solution Approach 2:
An identity management service acts as an intermediary layer between the complex backend systems and the user interface. This intermediary handles user profiling, authentication, and personalization logic, shielding users from system complexity while providing seamless personalized operation. The intermediary translates complex system capabilities into simple user-friendly interactions
3Adaptability or versatility
If digital twin systems do not integrate identity management, then device complexity is reduced, but adaptability and personalized service deteriorate
Solution Approach 1:
The identity management service provides multiple functions within a single integrated component: user authentication, profile management, permission control, and personalized interface configuration. This universal service handles diverse personalization requirements for different user types (drivers, passengers, fleet managers) through a unified system, reducing overall complexity compared to separate systems for each function
Data Source
AI summary
A system may include a digital twin management system to one of create, maintain, or render a digital twin based on received sensor data from one or more sensor systems associated with a vehicle. A system may include a digital twin simulation system to execute vehicle performance simulations using the digital twin by adjusting one or more vehicle performance parameters of the digital twin, and collect simulation outcome data resulting from the simulation. A system may include a cognitive process system to train machine learned models using vehicle performance simulation outcome data and makes predictions for providing decision support related to a simulated performance parameter to minimize a cost criterion associated with operating the vehicle.