Single-Shot Trajectory Prediction Using Composite Field Maps
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Solution Overview
Problem
Current trajectory prediction methods for robots and autonomous vehicles are inefficient, requiring multiple passes and increasing runtime with the number of agents in the scene, making them unsuitable for real-time applications in high-density environments.
Innovation Solution
A composite field-based single shot trajectory prediction system that uses a semantic segmenter, encoder, interaction module, and decoder to generate trajectory predictions in constant time, leveraging convolutional and deconvolutional layers, and non-local interaction blocks to capture spatio-temporal interactions among agents and the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple passes are used for trajectory prediction, then prediction accuracy is improved, but runtime increases with the number of agents
Solution Approach 1:
The prediction task is segmented into independent local interaction computations for each agent pair, allowing parallel processing. Each agent's trajectory prediction is computed independently based on local features and interactions, enabling the system to scale efficiently with the number of agents without increasing sequential runtime.
Solution Approach 2:
The system transitions from sequential multi-pass processing to a single-pass parallel computation model by introducing spatial dimensionality through grid-based local feature extraction. Multiple agents' trajectories are predicted simultaneously in one forward pass by leveraging parallel computation across different spatial locations and agent combinations.
2Measurement precision
If multiple passes are used for trajectory prediction, then prediction accuracy is improved, but real-time performance deteriorates
Solution Approach 1:
Local features for each agent and environment grid cell are pre-computed and stored before the prediction step. These pre-extracted features include agent attributes, environment characteristics, and spatial relationships, which are then rapidly combined during the single forward pass to generate accurate trajectory predictions without requiring multiple processing passes.
Solution Approach 2:
The system uses replicated computation patterns for each agent pair, where the same interaction computation template is copied and applied to all agent combinations. This allows the system to maintain consistent prediction quality across multiple agents while achieving parallel execution speed, as each agent's prediction follows the same computational blueprint.
3Quantity of substance
If trajectory prediction scales with number of agents, then coverage is improved, but computational complexity increases
Solution Approach 1:
The computational problem is segmented into localized interactions between pairs of agents rather than global multi-agent reasoning. Each agent's trajectory is determined by summing contributions from local environment features and interactions with other agents, reducing the computational complexity from exponential in the number of agents to linear or near-linear scaling.
Solution Approach 2:
A universal interaction computation module handles all agent pairs using the same computational framework. This multi-functional module processes any combination of agents and environment configurations through a single generalized algorithm, eliminating the need for agent-specific processing pipelines and reducing overall system complexity while maintaining scalability.
Data Source
AI summary
According to one aspect, composite field based single shot trajectory prediction may include receiving an image of an environment including a number of agents, extracting a set of features from the image, receiving the image of the environment, encoding a set of trajectories from the image, concatenating the set of features and the set of trajectories from the image to generate an interaction module input, receiving the interaction module input, encoding a set of interactions between the number of agents and between the number of agents and the environment, concatenating the set of interactions and a localization composite field map to generate a decoder input, receiving the decoder input, generating the localization composite field map and an association composite field map, and generating a set of trajectory predictions for the number of agents based on the localization composite field map and the association composite field map.