Trajectory Prediction Using Dynamic Candidate Future Intents
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Solution Overview
Problem
Predicting the future trajectory of agents in an environment, such as autonomous vehicles, is challenging due to the need to consider multiple candidate future intents and account for dynamic changes in the environment.
Innovation Solution
A system that processes scene data and dynamically generated candidate future intents to predict the future trajectory of a target agent, using a trained machine learning model that includes a scene encoder, candidate encoder, and predictor neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system attempts to cover the entire multi-modal space of candidate future intents at once, then the coverage of possible trajectories is comprehensive, but the accuracy of individual trajectory predictions deteriorates due to dilution of focus across too many possibilities
Solution Approach 1:
The system segments the entire multi-modal space of candidate future intents into multiple discrete candidate future intents, each representing a distinct possible action or route. This segmentation allows the system to process and predict each intent separately, maintaining high accuracy for individual predictions while collectively covering the full range of possible behaviors through the ensemble of segmented candidates.
2Measurement precision
If the system uses conventional approaches to predict trajectories, then the system complexity is lower, but the prediction accuracy deteriorates due to inability to dynamically adapt to current scene conditions
Solution Approach 1:
The system employs dynamic candidate future intents that are generated and updated based on current scene conditions and historical data, rather than using static pre-defined trajectories. This dynamic adaptation allows the system to maintain high prediction accuracy by adjusting candidate intents to match real-time environmental conditions, while the modular neural network architecture manages the resulting system complexity through structured processing of dynamic inputs.
Solution Approach 2:
The system introduces candidate future intents as intermediary representations between raw scene data and final trajectory predictions. These intermediaries encode semantic information about possible future actions and routes, allowing the system to process complex scene dynamics through structured intermediate representations, thereby improving accuracy while managing complexity through layered processing.
3Adaptability or versatility
If the system considers all possible action types simultaneously, then the overall behavior coverage is complete, but the prediction accuracy for individual action types deteriorates due to mode collapse to a single dominant action type
Solution Approach 1:
The system segments different action types into separate candidate future intents, each with its own dedicated prediction pathway through the neural network. This segmentation prevents mode collapse by ensuring that each action type is evaluated independently with its own learned parameters and predictions, while the system maintains complete action type coverage by including diverse candidates across all possible action categories in the candidate set.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using candidate future intents.