Vehicle State Prediction for Low-Traffic Digital Twin Sync
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Solution Overview
Problem
Conventional digital twin systems face a trade-off between increasing reproduction accuracy and communication traffic volume, leading to insufficient data transmission or reduced accuracy in vehicle state reproduction.
Innovation Solution
A vehicle-side prediction system using machine learning to predict future states based on detection data, transmitting only necessary information to a server for simulation, reducing unnecessary communication and maintaining high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more real-time information is transmitted to increase reproduction accuracy, then the accuracy of vehicle state reproduction is improved, but the volume of communication traffic increases
Solution Approach 1:
The system performs prediction of future vehicle states in advance using a trained model before transmission. The prediction section generates predicted future states based on current detection data, and only necessary prediction results are transmitted to the server. This preliminary processing reduces the need for continuous transmission of raw detection data while maintaining high reproduction accuracy.
Solution Approach 2:
The system extracts only the essential information needed for accurate reproduction - specifically, prediction results of future states - rather than transmitting all raw detection data. The communication section selectively transmits usage information based on prediction results, filtering out redundant data and keeping communication traffic volume low while preserving reproduction accuracy.
2Reliability
If communication traffic is reduced to prevent data transmission issues, then communication reliability is improved, but the accuracy of vehicle state reproduction deteriorates
Solution Approach 1:
The system changes the parameter being transmitted from raw detection data to processed prediction results. By transforming the data format and content to contain only essential future state predictions, the system reduces communication traffic volume while maintaining sufficient information for accurate reproduction, thereby improving communication reliability without sacrificing accuracy.
3Measurement precision
If detection data is transmitted frequently to maintain accurate reproduction, then reproduction accuracy is improved, but communication efficiency deteriorates
Solution Approach 1:
The prediction section performs preliminary processing of detection data to generate future state predictions before transmission. This advance processing allows the system to transmit condensed information at lower frequencies while maintaining accurate reproduction, significantly improving communication efficiency without sacrificing precision.
Solution Approach 2:
The system transmits only the partial information necessary for accurate reproduction - specifically, prediction results of future states - rather than transmitting complete detection data sets. This selective transmission reduces communication frequency and traffic volume while maintaining sufficient accuracy for the digital twin system.
Data Source
AI summary
A vehicle (101) includes: a plurality of sensors (S); an acquisition section (11) that acquires detection data from the plurality of sensors (S); a prediction section (12) that uses a trained model (31) to predict a future state of the vehicle (101) on the basis of detection data (D1) and that outputs a prediction result for the future state as prediction data (D2); and a communication section (2) that transmits, to a server 102, usage information which is based on either the detection data (D1) or the prediction data (D2), after transmitting the usage information, the communication section (2) transmitting subsequent usage information on the basis of a determination result indicating whether it is possible to transmit the subsequent usage information.