Mobility Digital Twin Guidance for Active Road Surface Maintenance
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Solution Overview
Problem
Existing road surface maintenance systems operate passively, reacting only after detecting deficiencies, failing to provide proactive guidance to vehicles to avoid excessive loads on certain areas of the road surface.
Innovation Solution
An active road surface maintenance system utilizing a Mobility Digital Twin (MDT) framework that leverages sensing technology on humans, vehicles, and roads to predict future road conditions, model 'road digital twins', and generate guidance for vehicles to avoid problematic areas, supported by machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passive road surface maintenance systems are used, then system complexity is reduced, but the ability to provide proactive guidance to vehicles is lost
Solution Approach 1:
A cloud-based digital twin platform serves as an intermediary between road sensors and vehicle systems. The platform receives road condition data from sensors, creates digital twins of road surfaces, predicts future conditions, and generates guidance instructions for vehicles. This intermediary approach enables proactive guidance without requiring complex direct communication infrastructure between all road sensors and vehicles.
Solution Approach 2:
The system creates digital copies (digital twins) of physical road surfaces that simulate future conditions. These digital twins are generated by processing sensor data through machine learning models that predict road deterioration, crack formation, and other surface issues. The digital copies enable proactive identification of problematic areas before actual damage occurs, guiding vehicles away without needing complex real-time control systems.
2Measurement precision
If real-time sensor data collection is implemented, then road condition monitoring accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from raw sensor data for digital twin generation. Instead of processing all raw sensor data in real-time, the system identifies and extracts key indicators of road condition (such as surface temperature, moisture content, structural integrity metrics) and uses these extracted features to update digital twins. This reduces data processing requirements while maintaining monitoring accuracy.
Solution Approach 2:
The system performs preliminary data processing and feature extraction at the edge devices (road sensors and gateways) before transmitting data to the cloud platform. This preliminary action reduces the volume of data that needs to be transmitted and processed centrally, while still enabling accurate road condition monitoring through pre-processed feature data.
3Reliability
If machine learning algorithms are used for prediction, then predictive maintenance capability is improved, but computational requirements increase
Solution Approach 1:
The system applies machine learning algorithms selectively rather than continuously. Digital twins are updated at appropriate intervals based on road condition changes and traffic patterns, rather than requiring constant computational processing. This partial action approach maintains predictive maintenance capability while reducing overall computational energy consumption compared to continuous real-time prediction.
Data Source
AI summary
An active road surface maintenance system and method developed for connected vehicles with the aid of a mobility digital twin (MDT) framework. A method performed in a cloud-based digital space includes receiving data regarding a physical object from a physical space connected to a vehicle. The method also includes processing the data using machine learning to model road surface conditions, in which respective penalty values are assigned to corresponding road surfaces, a respective penalty value being higher the lower a condition of the corresponding road surface. The method also includes deriving instructions based on the modeled road surface conditions and the respective penalty values to guide actuation of the vehicle along a trajectory. The method further includes transmitting the instructions to the physical space connected to the vehicle to guide actuation of the vehicle.