Graph Neural Network Feature Extraction for Heterogeneous Traffic Data
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Solution Overview
Problem
Deep learning technologies in autonomous driving face challenges due to the varying quality and specifications of data from different sensors and sources, leading to suboptimal performance in environment modeling and feature extraction for traffic scenarios.
Innovation Solution
A method using graph neural networks to establish uniformly defined data representations, construct graphs that describe temporal and spatial relationships, and perform learning to extract features, which are then used to optimize the graph neural network for higher abstraction, robustness, and compatibility across different data specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning technologies are used with data from different sensors and sources, then the coverage and applicability of the system is improved, but the quality and specification variability of the data deteriorates, leading to negative impact on deep learning performance
Solution Approach 1:
The patent segments the heterogeneous data processing into distinct modules: a data representation module that converts different sensor data types into unified representations, and a graph neural network module that processes these representations. This segmentation allows the system to handle diverse data sources while maintaining consistent processing quality.
Solution Approach 2:
The patent introduces uniformly defined data representations as an intermediary layer between raw sensor data and the deep learning model. This intermediary standardizes the data format, enabling the graph neural network to process data from different sources reliably without being affected by source-specific variations.
2Measurement precision
If manually designed model construction is used for feature extraction, then the model can be optimized for specific traffic scenarios, but the adaptability to different scenarios and data sources deteriorates
Solution Approach 1:
The patent employs a graph neural network as a universal feature extraction framework that can handle multiple types of traffic scenario data (images, point clouds, maps, sensor data) through a unified graph representation. This multi-functional approach maintains high extraction accuracy across different scenarios without requiring scenario-specific manual model design.
Solution Approach 2:
The patent transforms the input data into graph structures with nodes representing entities and edges representing relationships, changing the parameter representation from raw sensor formats to a standardized graph format. This parameter transformation enables the same graph neural network model to effectively process diverse traffic scenario data with high accuracy.
3Productivity
If structural optimization is applied for effective information extraction, then the information extraction efficiency is improved, but the robustness and high-level abstraction capability deteriorates
Solution Approach 1:
The patent transitions from traditional structural optimization in fixed architectures to a graph-based dimensional representation where data is organized in terms of entities and relationships. This dimensional change enables the graph neural network to achieve both efficient information extraction through graph traversal and high-level abstraction through learned graph representations.
Solution Approach 2:
The patent replaces manual structural optimization mechanisms with a learned graph neural network system. Instead of manually designing extraction structures, the system automatically learns optimal feature representations from the graph data, achieving both efficiency and robustness through adaptive learning rather than fixed mechanical structures.
Data Source
AI summary
A method related to the field of environment modeling of traffic scenarios is disclosed. Specifically, a method for extracting features from data of a traffic scenario based on a graph neural network is disclosed. The method includes the following steps: step (S1): establishing uniformly defined data representations for the data of the traffic scenario; step (S2): constructing a graph based on the data of the traffic scenario that has the uniformly defined data representations, where the graph describes a temporal and/or spatial relationship between entities in the traffic scenario; and step (S3): using the constructed graph as an input of the graph neural network to perform learning on the graph neural network, such that the features are extracted from the data of the traffic scenario. A device for extracting features from data of a traffic scenario based on a graph neural network and a computer program product is also disclosed.