Vehicle Cluster Edge Computing for Duplicate Sensor Data Reduction
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Solution Overview
Problem
Autonomous vehicles generate vast amounts of data when traveling together, leading to high network bandwidth usage and server load, as each vehicle processes and transmits duplicate data individually to remote cloud servers, which is inefficient and impractical.
Innovation Solution
Autonomous vehicles are grouped into clusters, where data is collected and processed locally by a reference vehicle, with deduplication and analysis to generate driving instructions, reducing the need for duplicate data transmission to remote servers through edge computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each autonomous vehicle processes and transmits data individually to remote cloud servers, then comprehensive data analysis can be performed, but network bandwidth usage and server load increase significantly
Solution Approach 1:
The system segments vehicles into clusters based on trajectory similarity, with each cluster processing data locally through a reference vehicle. This segmentation reduces the overall network load by handling data processing distributed across multiple edge nodes rather than concentrating all processing at remote cloud servers.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by establishing hierarchical levels: individual vehicles, cluster-level reference vehicles, and remote cloud servers. Data is processed at the closest appropriate level, with only aggregated or essential data transmitted upward, effectively adding a dimensional layer to the processing architecture.
2Reliability
If each autonomous vehicle transmits duplicate sensor data to remote servers, then complete environmental information is available for analysis, but power consumption increases
Solution Approach 1:
Vehicles traveling along common trajectories are merged into clusters where their sensor data is combined and processed together. The reference vehicle aggregates data from multiple vehicles, eliminating redundant transmissions and reducing total power consumption while maintaining environmental data completeness through data fusion.
Solution Approach 2:
Instead of each vehicle transmitting identical environmental observations to remote servers, the system uses the reference vehicle to create a representative copy of cluster data. This single copy is then transmitted to remote servers, reducing transmission power requirements while preserving the essential environmental information for analysis.
3Device complexity
If all vehicle data is processed by remote cloud servers, then centralized control is maintained, but real-time processing speed decreases
Solution Approach 1:
The centralized processing architecture is segmented into distributed edge computing nodes (reference vehicles) that handle real-time processing locally. This segmentation enables parallel processing across multiple nodes, significantly increasing overall processing speed while maintaining centralized coordination through the cloud server for non-time-critical operations.
Solution Approach 2:
Reference vehicles act as intermediary nodes between individual vehicles and remote cloud servers. These intermediaries perform real-time data processing and filtering, providing rapid local responses while maintaining the centralized architecture's coordination capabilities through selective data transmission to cloud servers.
Data Source
AI summary
Autonomous vehicle communications are managed by assigning vehicle clusters to process collected data as a unified cluster, whether transmitting the data to a remote server or processing the data by an assigned vehicle within the cluster. Efficient travel guidance is produced in a timely manner by reducing the network bandwidth usage and volume of data transferred by autonomous vehicles traveling on a roadway with other autonomous vehicles.