Predictive System for Dynamic Interaction Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing models for predicting the dynamics of interacting physical objects assume stationary relationships, which is not accurate in dynamic systems like traffic situations where interactions change over time, leading to inaccurate predictions.
Innovation Solution
A system that models latent connectivity dynamics by capturing pairwise interactions between objects as sequences of interaction types, using encoder and decoder models with propagation models to predict future interactions and classify past interactions, allowing for time-varying configurations and more accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If stationary relationship assumptions are used in interaction models, then model simplicity is maintained, but prediction accuracy deteriorates in dynamic systems
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from static interaction models to dynamic interaction models that adapt to changing system states. The neural relational inference model is enhanced to capture time-varying interaction configurations, allowing the model to adjust its parameters and structure based on the current state of the system, thereby improving prediction accuracy in dynamic environments while maintaining manageable complexity through structured approaches.
2Measurement precision
If time-varying interaction configurations are modeled, then prediction accuracy improves, but model complexity increases
Solution Approach 1:
The patent applies the Segmentation principle by decomposing the complex task of modeling time-varying interactions into distinct components: an encoder module that processes individual object trajectories, an interaction module that computes pairwise interactions, and a decoder module that predicts future states. This modular segmentation allows the model to capture dynamic interactions accurately while managing complexity through organized, reusable components that can be trained and updated independently.
3Measurement precision
If discrete interaction types are learned for each object pair, then interaction classification accuracy improves, but computational requirements increase
Solution Approach 1:
The patent applies the Parameter changes principle by representing discrete interaction types as continuous probability distributions over a predefined set of interaction categories. Instead of directly outputting discrete labels, the model outputs softmax probabilities that indicate the likelihood of each interaction type. This continuous representation allows for more nuanced classification, handles uncertainty gracefully, and enables gradient-based optimization, improving classification accuracy while maintaining computational efficiency through vectorized operations.
Data Source
Figure 1
Figure 2~3
Figure 4~5
AI summary
The invention relates to a system (100) for predicting object feature vectors of multiple interacting physical objects. The system uses a decoder model, which includes a prediction model and of a set of propagation models for a set of multiple interaction types; and observation data representing sequences of observed object feature vectors of the interacting objects. For a sequence of object feature vectors to be predicted of a first interacting object, a sequence of corresponding pairwise interaction types from the set of multiple interaction types is obtained. Propagation data from a second object to a first object is determined using the propagation model indicated by the sequence of interaction types. The object feature vector is predicted using a given prediction model based on at least the determined propagation data.