Neural Trajectory Prediction With Knowledge Fusion for Sparse Data
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Solution Overview
Problem
Current neural network-based trajectory prediction systems require extensive training data and are not flexible enough to incorporate knowledge-driven or rule-based predictors, limiting their applicability in scenarios with insufficient data or complex traffic situations.
Innovation Solution
A method and system that combines time-dependent sensor information with rule- or knowledge-based information using an auto-encoder and fusion network to project expert predictions into the latent space of a main neural network, allowing for reduced training data requirements and improved prediction accuracy by fusing data-driven and knowledge-driven information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for trajectory prediction, then prediction accuracy can be improved, but the requirement for training data quantity increases significantly
Solution Approach 1:
The patent combines data-driven neural network predictions with knowledge-driven rule-based predictions into a unified hybrid prediction system. The neural network processes sensor data while the rule-based system incorporates domain knowledge and constraints, and both outputs are fused to produce the final trajectory prediction. This merging allows the system to achieve high prediction accuracy without requiring extremely large training datasets, as the rule-based component provides guidance where data is scarce.
Solution Approach 2:
The patent introduces a loss function as an intermediary that bridges the neural network output and the rule-based predictions. The loss function is designed to incorporate domain knowledge and constraints, guiding the neural network learning process without requiring extensive training data. This intermediary mechanism allows the system to leverage both data-driven and knowledge-driven approaches effectively.
2Adaptability or versatility
If traditional neural network approaches are used, then numerical processing is straightforward, but flexibility to incorporate knowledge-driven predictors is reduced
Solution Approach 1:
The patent segments the prediction system into distinct modular components: a neural network module for processing sensor data, a rule-based module for incorporating domain knowledge, and a fusion module for combining their outputs. This segmentation allows each component to be developed and optimized independently while maintaining overall system flexibility. The modular architecture enables easy integration of different types of predictors without redesigning the entire system.
3Reliability
If multiple prediction systems are incorporated, then prediction robustness improves, but alignment and architecture evaluation complexity increases
Solution Approach 1:
The patent designs a universal loss function that can accommodate multiple different prediction systems and types of domain knowledge. This loss function serves multiple purposes: it guides the neural network learning, incorporates rule-based constraints, and enables seamless integration of various prediction models. The universal nature of this loss function simplifies the architecture by providing a common interface for integrating diverse prediction systems, reducing the complexity of alignment and evaluation.
Data Source
AI summary
A computer-implemented method for predicting trajectories is disclosed based on a main neural network by fusing data-driven and knowledge-driven features. The method includes: receiving first input information as time-dependent numerical information; receiving second input information, as rule- or knowledge-based information including one or more trajectory prediction information; processing second input information by using an auto-encoder configured to encode the second input information by extracting features from the second input information, thereby obtaining encoded second input information; providing the encoded second input information to a fusion network, the fusion network providing transformed information obtained by transforming encoded second input information according to properties of the main neural network; providing the first input information and the transformed information to the main neural network, the main neural network fusing the first input information and the transformed information in order to provide trajectory predictions based thereon; and outputting the trajectory prediction.