Edge Data Center Digital Traffic Model for Vehicle Trajectory
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Solution Overview
Problem
In complex traffic situations, vehicles with autonomous driving capabilities face challenges due to discrepancies in digital traffic models caused by transmission delays, leading to potential collisions and reduced operational safety and comfort.
Innovation Solution
A method where an edge data center connected to a radio access network updates digital traffic models with vehicle data, accounting for transmission delays to calculate and transmit accurate trajectory data, ensuring vehicles operate safely and comfortably by minimizing sudden accelerations and collision risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a stationary server centrally calculates trajectories of vehicles based on a digital traffic model, then trajectory conflicts between vehicles are resolved, but transmission delays cause the digital traffic model to become outdated, reducing operational safety
Solution Approach 1:
The system performs preliminary actions by predicting future positions of vehicles based on current trajectories and speeds. The digital traffic model is updated with predicted positions that account for transmission delays, allowing the stationary server to calculate trajectories based on anticipated rather than outdated traffic situations. This preliminary update compensates for the time offset inherent in centralized processing.
Solution Approach 2:
The system implements feedback mechanisms where vehicles continuously transmit their actual positions and trajectories to the stationary server. The server compares received data with predicted positions, detects deviations, and recalculates trajectories in real-time. This continuous feedback loop ensures that despite transmission delays, the digital traffic model remains synchronized with the actual traffic situation, maintaining operational safety.
2Measurement precision
If vehicles transmit vehicle data continuously to the edge data center, then the digital traffic model remains accurate, but communication bandwidth consumption increases
Solution Approach 1:
The system applies local quality by selectively transmitting data based on vehicle relevance to the ego vehicle. Only vehicles within a defined relevance radius and exhibiting significant motion (above threshold speed or acceleration) transmit their data continuously. Other vehicles are represented by periodic updates or predicted positions. This selective approach maintains digital traffic model accuracy for critical vehicles while reducing overall bandwidth consumption.
Solution Approach 2:
The system uses partial action by transmitting only the most critical vehicle data continuously, while using predictive models for less critical vehicles. The edge data center receives complete data sets only when necessary (e.g., when a vehicle enters the relevance radius or exhibits abnormal behavior), and uses interpolated or predicted data otherwise. This partial transmission strategy maintains model accuracy where needed while minimizing bandwidth usage.
Data Source
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AI summary
A method for operating a vehicle, wherein a vehicle transmits vehicle data to an edge data center being connected to a radio access network via a wireless connection being established by a communication unit of the vehicle; the edge data center updates a digital traffic model of a traffic situation involving the vehicle with the transmitted vehicle data, calculates a trajectory of the vehicle based on the updated digital traffic model and transmits trajectory data of the calculated trajectory to the vehicle via the wireless connection; and the vehicle is operated according to the transmitted trajectory data, a vehicle and a computer program product.