Connected Vehicle Feature Sharing via Channel Attention
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Solution Overview
Problem
Connected vehicle systems face challenges in large-scale deployment due to limited communication bandwidth and increasing dataset sizes, making it impractical to transmit raw or even cropped sensory data effectively.
Innovation Solution
A method for deep cooperative feature sharing among connected vehicles, where sensor data is encoded into structured data with channels, and a channel attention map is generated to select and transmit only the most relevant channels, reducing bandwidth consumption and improving data quality through machine learning-based channel selection and fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all raw sensory data is transmitted between connected vehicles, then complete information is shared, but bandwidth consumption increases and becomes impractical for large-scale deployment
Solution Approach 1:
The patent extracts only the most relevant and informative features from raw sensory data using deep learning models. Instead of transmitting complete raw data, the system identifies and transmits only essential features that capture the critical information needed for connected vehicle applications, thereby reducing bandwidth consumption while maintaining information quality.
Solution Approach 2:
The patent applies different processing and transmission strategies to different portions of sensory data based on their importance. High-importance regions and features are transmitted with higher fidelity, while less critical areas are compressed or omitted, optimizing the trade-off between information completeness and bandwidth usage.
2Loss of energy
If cropped sensory data is transmitted based on pre-defined principles, then bandwidth is reduced, but data quality and relevance are compromised
Solution Approach 1:
The patent performs preliminary processing of sensory data through deep learning models before transmission. The system pre-identifies important features, generates attention maps, and selects relevant channels in advance, so that only high-quality, relevant data is transmitted. This preliminary action ensures data quality is maintained while bandwidth is optimized.
Solution Approach 2:
The patent employs attention mechanisms that dynamically adjust which features are transmitted based on the current context and requirements. The system provides feedback loops where transmission decisions are continuously optimized based on perceived importance, ensuring high data quality for critical information while minimizing bandwidth usage.
3Measurement precision
If deep learning models are used for feature selection, then data relevance is improved, but computational complexity increases
Solution Approach 1:
The patent segments the deep learning processing into distributed components across multiple vehicles. Each vehicle runs local deep learning models to extract features from its own sensory data, and only the extracted features (not raw data) are transmitted. This segmentation reduces the computational burden on any single vehicle while maintaining high feature relevance.
Solution Approach 2:
The patent introduces attention maps and feature encoding as intermediary representations between raw sensory data and transmitted information. These intermediaries compress complex sensory data into compact, relevant feature vectors that capture essential information with reduced computational requirements for transmission and processing.
Data Source
AI summary
A method for data sharing between a transmitter vehicle and a receiver vehicle includes obtaining sensor data, encoding the sensor data into structured data having a set of features and a set of channels, generating a channel attention map based on inter-channel relationships of the set of features, generating weights corresponding to channels of the channel attention map, selecting one or more channels among the set of channels based on the weights corresponding to the set of channels, and transmitting the selected channels to the receiver vehicle.