Spatial Graph Trajectory Prediction Under Environmental Constraints
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Solution Overview
Problem
Current trajectory prediction methods for autonomous navigation in interactive environments fail to accurately account for physical constraints, leading to implausible predictions due to weak consideration of environmental influences in human-space interactions.
Innovation Solution
A computer-implemented method that processes image data to create an agent-augmented static representation of the environment, constructing spatial graphs that account for spatial and temporal interactions, allowing for the prediction of future agent trajectories by clustering nodes in these graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing trajectory prediction methods are used that focus on human-human interactions, then social aspects of human behaviors can be modeled, but physical constraints and environmental influences are overlooked leading to implausible predictions
Solution Approach 1:
The system segments the environment into discrete spatial cells and represents agents as nodes in a graph structure. This segmentation allows the model to separately process social interactions between agents and physical constraints from the environment, then integrate them through the graph neural network to produce plausible trajectory predictions that satisfy both social and physical requirements.
Solution Approach 2:
The patent introduces an intermediary graph representation that connects agents to environmental cells. This graph structure acts as a mediator between social interaction models and physical constraint models, enabling the integration of both aspects through message passing and feature aggregation in the graph neural network architecture.
2Adaptability or versatility
If current methods model social interactions without environmental consideration, then human behavior patterns can be captured, but verification of physical functionality in human-space interaction is difficult
Solution Approach 1:
The patent adds a spatial dimension to traditional social interaction models by introducing environmental cells and graph-based spatial relationships. This dimensional extension allows the model to simultaneously capture social interaction patterns in the agent-agent dimension and physical constraints in the agent-environment dimension, enabling verification of both aspects.
Solution Approach 2:
The system creates a graph-based copy or representation of the physical environment and agent positions. This graphical copy allows for precise measurement and verification of physical constraints while preserving the social interaction dynamics, as the graph structure can simultaneously encode both social relationships and spatial configurations.
3Reliability
If trajectory prediction considers both social interactions and environmental constraints, then prediction plausibility improves, but computational complexity increases
Solution Approach 1:
The patent employs a dynamic graph neural network where the graph structure and node features are updated at each time step based on current agent positions and environmental interactions. This dynamic approach allows the system to adaptively compute only the necessary interactions at each step, improving computational efficiency while maintaining high prediction plausibility through continuous environmental constraint verification.
Data Source
AI summary
A system and method for completing trajectory prediction from agent-augmented environments that include receiving image data associated with surrounding environment of an ego agent and processing an agent-augmented static representation of the surrounding environment of the ego agent based on the image data. The system and method also include processing a set of spatial graphs that correspond to an observation time horizon based on the agent-augmented static representation. The system and method further include predicting future trajectories of agents that are located within the surrounding environment of the ego agent based on the spatial graphs.