Vehicle Digital Twin Interface for User Satisfaction-Based Settings
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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 user satisfaction and vehicle performance.
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
1Reliability
If a digital twin system is implemented for passenger vehicles, then vehicle performance monitoring and customer experience management are improved, but the interface complexity and configuration options increase
Solution Approach 1:
The digital twin interface is segmented into multiple views (e.g., performance view, maintenance view, customer experience view) that can be independently configured and displayed. Each view focuses on specific parameters and functions, reducing the perceived complexity while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The interface dynamically adapts its configuration based on user profiles, vehicle operating states, and customer preferences. The system automatically adjusts which parameters are displayed and how they are presented, reducing the need for manual configuration while maintaining reliability.
2Adaptability or versatility
If the digital twin interface is configured based on user profiles, then customer experience is enhanced, but the system complexity and configuration requirements increase
Solution Approach 1:
The system automatically creates and configures user profiles based on observed driving patterns, vehicle usage, and customer feedback. The digital twin self-adjusts interface configurations without requiring manual setup, enhancing adaptability while minimizing configuration complexity.
Solution Approach 2:
Default user profiles and interface configurations are pre-established based on common customer preferences and vehicle types. When a new user accesses the system, these preliminary configurations are automatically applied and can be fine-tuned, reducing the initial configuration burden.
3Ease of operation
If neural networks are used to detect user satisfaction states, then user satisfaction optimization is improved, but the computational requirements and processing time increase
Solution Approach 1:
The neural network processes only the most relevant user feedback signals and vehicle parameters at any given time, rather than analyzing all possible data points continuously. This partial processing approach maintains user satisfaction optimization while reducing computational energy consumption.
Solution Approach 2:
The system continuously monitors user satisfaction indicators and makes incremental adjustments to vehicle parameters based on neural network predictions. This continuous low-level processing is more energy-efficient than periodic high-intensity computation, maintaining optimization while managing energy use.
4Productivity
If real-time vehicle parameter optimization is implemented, then vehicle performance is improved, but the processing speed and response time requirements increase
Solution Approach 1:
The system optimizes only the most critical vehicle parameters in real-time (e.g., acceleration, braking, route selection) while maintaining preset configurations for less dynamic parameters. This localized optimization approach improves overall vehicle performance without requiring high-speed processing of all vehicle systems.
Solution Approach 2:
The digital twin system uses feedback from sensors and user interactions to continuously refine parameter optimization decisions. This feedback loop enables the system to learn from past performance and make faster, more accurate real-time adjustments without requiring excessive processing speed for each individual decision.
Data Source
AI summary
A system for representing multiple operating states of a vehicle includes a digital twin system configured to create and manage a digital twin of the vehicle. A data collection system is configured to receive one or more data inputs indicating vehicle parameter data of the vehicle. A state determination system is configured to determine multiple operating states of the vehicle based on the vehicle parameter data. An interface system is configured to present the multiple operating states of the vehicle to a user via the digital twin of the vehicle.