Vehicle Interior Command Update via Adaptive Digital Twin Interface
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Solution Overview
Problem
Current digital twin systems for passenger vehicles are limited in their ability to enhance customer experience, with primitive interfaces that do not offer customization options based on user profiles, and lack advanced features for managing vehicle states such as maintenance, energy utilization, navigation, and driver satisfaction.
Innovation Solution
A configurable digital twin interface system that uses neural networks to detect and optimize vehicle operating states based on user satisfaction, integrating with edge intelligence and 5G connectivity to provide personalized experiences, including real-time data from sensors and AI-driven configuration for improved situational awareness and vehicle performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a digital twin system is implemented for passenger vehicles, then vehicle state monitoring and simulation capabilities are improved, but the interface remains primitive and lacks customization options
Solution Approach 1:
The interface dynamically adapts its configuration based on user profiles and preferences. The system allows users to customize which vehicle states are displayed and how they are presented, transforming the static primitive interface into a dynamic, user-adaptive interface that improves usability while maintaining monitoring capabilities
Solution Approach 2:
The interface configuration parameters are changed based on user profiles. Different users can have different interface configurations showing different sets of vehicle states, different visualization styles, and different levels of detail, thereby improving ease of operation without sacrificing the reliability of vehicle state monitoring
2Adaptability or versatility
If the digital twin interface is customized based on user profiles, then user experience is improved, but system complexity increases
Solution Approach 1:
The system automatically configures the interface based on user profiles without requiring manual setup. The digital twin system self-adjusts the interface configuration according to stored user preferences and characteristics, providing adaptability while avoiding the complexity of manual configuration management
Solution Approach 2:
A single interface framework serves multiple user profiles with different customization needs. The system uses a universal interface structure that can be configured for different users through parameters and settings, achieving versatility without proportionally increasing system complexity
3Measurement precision
If neural networks are used to detect driver satisfaction, then driver state monitoring is improved, but energy consumption increases
Solution Approach 1:
The neural network processes only the most relevant sensor data for detecting driver satisfaction rather than analyzing all available vehicle data. This partial processing approach maintains measurement precision for the specific function of satisfaction detection while reducing overall energy consumption compared to comprehensive data analysis
4Loss of information
If real-time vehicle state data is provided to users, then situational awareness is improved, but information processing requirements increase
Solution Approach 1:
The interface presents different levels of information detail to different users based on their profiles and needs. Each user receives customized information about vehicle states that is relevant to their specific context, providing adequate situational awareness without uniformly processing and displaying all possible data for all users, thereby reducing overall processing requirements
Data Source
AI summary
A method for controlling a transportation system includes using an output from a device within a vehicle interior to update a command input to a transportation system in response to detecting a triggering condition from the device within the vehicle interior affecting the transportation system.