Knowledge Graph for Traffic Motion Prediction
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Solution Overview
Problem
Current deep learning approaches for traffic motion prediction rely on limited subsets of contextual information due to the lack of comprehensive representations in prominent datasets, leading to issues with robustness, explainability, and generalization.
Innovation Solution
A computer-implemented method for generating a knowledge graph for traffic motion prediction, which integrates environment sensor data and map data to create a comprehensive representation of traffic participants and their motion tracks, using nodes and edges to organize information about road networks, traffic participants, and their features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained end-to-end from raw data, then prediction accuracy can be achieved, but robustness and generalization capability deteriorate due to lack of causal understanding and structured knowledge
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary between raw sensor data and deep learning models. The knowledge graph structures contextual information (road topology, traffic rules, motion tracks) into a causal framework that guides the neural network, enabling the model to learn from structured relationships rather than raw pixels alone, thus improving robustness while maintaining accuracy
Solution Approach 2:
The system performs preliminary structuring of contextual information into a knowledge graph before feeding data to the deep learning model. By pre-organizing traffic rules, road topology, and motion tracks into structured relationships, the model receives processed semantic knowledge that enhances its ability to generalize to new scenarios without retraining
2Reliability
If comprehensive contextual information is integrated into the model, then prediction reliability improves, but system complexity increases due to multiple data sources and processing requirements
Solution Approach 1:
The patent segments the complex processing task into distinct modules: (1) knowledge graph construction from map data and sensor data, (2) graph neural network processing of structured relationships, and (3) trajectory prediction. This modular segmentation manages complexity by handling different data types and processing stages separately while maintaining their integrated benefit
3Adaptability or versatility
If structured knowledge representation is implemented, then explainability and generalization improve, but data processing time increases due to knowledge graph construction and maintenance
Solution Approach 1:
The knowledge graph is constructed in advance from map data and sensor data, organizing contextual information into structured relationships before prediction is needed. This preliminary structuring allows the graph neural network to efficiently query pre-organized knowledge during real-time prediction, reducing inference time while maintaining explainability
Data Source
AI summary
A computer-implemented method for generating a knowledge graph for traffic motion prediction. The method includes: receiving environment sensor data of at least one environment sensor of an ego-vehicle; receiving map data from an electronic road map; extracting the information regarding the at least one traffic participant from the environment sensor data and extracting the information regarding the motion track the traffic participant is positioned on from the map data; and generating a knowledge graph of the road network in the environment of the ego-vehicle including nodes and edges based on the map data and/or the environment sensor data. The knowledge graph includes at least one node representing the traffic participant and at least one node representing the lane the traffic participant is positioned on.