Joint Neural Trajectory Prediction for Scene-Consistent Multi-Agent Paths
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing trajectory prediction methods in environments with multiple entities require significant computing resources and often fail to maintain scene consistency, leading to inefficiencies in memory and time usage.
Innovation Solution
A scene-consistent policy-based trajectory prediction model using neural networks that generate joint trajectory predictions for multiple interacting agents, incorporating a policy network and a Gibbs distribution to ensure scene consistency and reduce dimensionality, while utilizing a tunable risk measure to enhance training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional trajectory prediction methods are used for multiple entities, then trajectory predictions can be generated, but significant computing resources and memory are required
Solution Approach 1:
The patent segments the trajectory prediction problem by separating entity-specific features from scene-level context. Each entity is processed with its own neural network branch that captures individual characteristics, while a shared scene representation captures global context. This segmentation allows independent optimization of entity-specific computations while sharing scene-level computations across all entities, reducing overall computational resources required.
Solution Approach 2:
The patent merges entity-specific trajectory predictions with scene-level context through a unified neural network architecture. The scene representation is combined with individual entity features to generate joint trajectory predictions that account for both individual entity behavior and scene-wide interactions. This merging enables the system to maintain scene consistency while efficiently utilizing shared computational resources.
2Productivity
If traditional trajectory prediction methods are used for multiple entities, then trajectory predictions can be generated, but memory usage increases significantly
Solution Approach 1:
The patent segments memory usage by storing entity-specific features separately from scene-level context. Each entity maintains its own feature representation in dedicated memory slots, while the shared scene representation is stored once and reused across all entity predictions. This segmentation reduces redundant memory storage and improves memory access efficiency during trajectory prediction.
Solution Approach 2:
The scene representation serves multiple functions simultaneously: it provides global context for all entity predictions, captures scene-wide interactions, and can be reused across different prediction scenarios. This multi-functional scene representation reduces the need for separate memory allocations for different prediction tasks, optimizing overall memory usage.
3Reliability
If traditional trajectory prediction methods are used, then predictions can be generated, but scene consistency is not maintained
Solution Approach 1:
The patent merges entity-specific predictions with scene-level context through a unified neural network architecture. The scene representation is integrated with individual entity features to generate joint trajectory predictions that inherently maintain scene consistency. By combining these elements at the network architecture level rather than through post-processing, the model ensures that all entity predictions are mutually consistent with the scene context.
Solution Approach 2:
The patent implements feedback mechanisms where the scene representation is continuously updated based on predicted trajectories, and these updated scene representations feed back into the prediction process. This feedback loop ensures that predictions remain consistent with the evolving scene state, allowing the model to adapt to changing conditions while maintaining overall scene consistency without requiring excessively complex control mechanisms.
Data Source
AI summary
Apparatuses, systems, and techniques to generate trajectory predictions. In at least one embodiment, trajectory predictions are generated based on, for example, one or more neural networks.