Multi-Agent Trajectory Prediction Using Gibbs-Sampled Policy Networks
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Solution Overview
Problem
Existing trajectory prediction systems require significant computing resources and struggle with maintaining scene consistency and agent interaction accuracy, particularly in complex environments with multiple entities.
Innovation Solution
A scene-consistent policy-based trajectory prediction model using neural networks that generate joint trajectory predictions for multiple agents, incorporating a policy network and a Gibbs distribution to ensure scene consistency and agent interaction accuracy, while reducing computational complexity through efficient partitioning and diversity sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trajectory prediction systems are used to predict trajectories of multiple entities, then trajectory predictions can be obtained, but significant computing resources are required and scene consistency deteriorates
Solution Approach 1:
The system segments the set of entities into multiple groups, where each group contains a subset of entities. A separate neural network is trained for each group to predict trajectories only for entities within that group. This segmentation reduces the computational complexity of predicting all entities simultaneously while maintaining scene consistency through the grouping strategy and the use of Gibbs distribution for joint trajectory sampling.
2Productivity
If trajectory prediction is performed for multiple entities, then comprehensive coverage is achieved, but computing resources increase significantly
Solution Approach 1:
The system divides the computation task by segmenting entities into groups and training separate neural networks for each group. This allows comprehensive trajectory prediction coverage across all entities while reducing the computational burden on each individual network, as each network only processes a subset of entities rather than all entities simultaneously.
Solution Approach 2:
The system uses Gibbs distribution to sample joint trajectories from the product space of individual group trajectories. By sampling a representative subset of joint trajectory combinations rather than exhaustively computing all possible combinations, the system achieves comprehensive coverage with reduced computational resources.
3Measurement precision
If scene consistency is maintained through joint trajectory prediction, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system maintains scene consistency by segmenting entities into groups and using Gibbs distribution to sample joint trajectories that respect inter-group interactions. Each neural network predicts trajectories for its assigned group independently, but the Gibbs sampling ensures that joint trajectories are consistent across groups, achieving accurate agent interaction prediction without the full computational complexity of a single monolithic model.
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.