Connected Vehicle Deployment Using Digital Twin Prediction
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Solution Overview
Problem
Connected vehicle ecosystems face disruptions due to contextual situations such as network outages or weather conditions, leading to unreliable Vehicle-to-Vehicle (V2V) communication and potential service failures, as a single vehicle may not be able to maintain the required quality of service.
Innovation Solution
A proactive connected vehicle deployment engine uses a digital twin simulation and prediction engine to identify the minimum number of vehicles needed to maintain services and proactively deploys additional vehicles to ensure continuous V2V connectivity, leveraging blockchain for tracking and smart contracts to manage service agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single vehicle is used to provide connected vehicle services, then device complexity is reduced, but reliability deteriorates due to disruptions from network outages or weather conditions
Solution Approach 1:
The system performs simulation and prediction of contextual situations in advance to identify potential disruptions before they occur. By proactively determining the minimum number of vehicles needed and deploying them beforehand, the system ensures service continuity while avoiding the complexity of reactive responses to failures.
Solution Approach 2:
The system creates a digital twin simulation model that replicates the connected vehicle ecosystem. This virtual copy allows for testing and prediction of various contextual situations without affecting the real system, enabling reliable decision-making about vehicle deployment while keeping actual system complexity manageable.
2Reliability
If additional vehicles are deployed proactively based on simulation and prediction, then reliability of V2V connectivity is improved, but loss of time for data collection and simulation increases
Solution Approach 1:
The system performs simulation and prediction on essential contextual parameters rather than exhaustively analyzing all possible variables. By focusing on the most critical factors that affect V2V connectivity, the system achieves reliable predictions without the time cost of complete system simulation.
Solution Approach 2:
The system continuously collects and pre-processes real-world data from sensors, maintaining an up-to-date simulation model ready for rapid prediction. This preliminary data preparation reduces the time required for actual prediction when deployment decisions are needed.
3Productivity
If the minimum number of vehicles is dynamically determined based on contextual situations, then service quality is improved, but device complexity for calculating minimum vehicle requirements increases
Solution Approach 1:
The system uses simulation results and predicted contextual situations as feedback to dynamically adjust the minimum vehicle requirement calculations. This feedback loop enables the system to optimize service efficiency by matching vehicle deployment to actual needs while managing complexity through iterative refinement rather than complex upfront modeling.
Solution Approach 2:
The system changes key parameters in the simulation model based on predicted contextual situations, such as network conditions, weather patterns, and traffic density. By adjusting these parameters dynamically, the system determines appropriate vehicle requirements without building a permanently complex calculation system for all possible scenarios.
Data Source
AI summary
A mechanism is provided in a data processing system for proactive deployment of connected vehicles to a connected vehicle ecosystem based on computerized simulation and prediction of contextual situations in accordance with an illustrative embodiment. The mechanism collects real-world data from sensors in a connected vehicle ecosystem and simulates the connected vehicle ecosystem based on the real-world data and a simulation model data of the connected vehicle ecosystem. The mechanism predicts changes in contextual situations in the connected vehicle ecosystem that affect connected vehicle services provided in the connected vehicle ecosystem based on the simulation of the connected vehicle ecosystem, the real-world data, and the simulation model data. The mechanism identifies a minimum number of connected vehicles required to provide the connected vehicle services and deploys one or more additional connected vehicles to the connected vehicle ecosystem based on the predicted changes in contextual situations and the minimum number of connected vehicles required to provide the connected vehicle services.