Multi-Agent Trajectory Forecasting With Generative Memory
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Solution Overview
Problem
Current autonomous driving systems face challenges in predicting multi-agent trajectories due to complex road geometries and varied traffic behaviors, leading to inefficient and unsafe navigation, particularly exacerbated by the phenomenon of catastrophic forgetting where models prioritize recent over past interactions, resulting in underutilization of learned data.
Innovation Solution
A computer-implemented method and system utilizing a graph-based conditional generative memory system that processes scene graphs and aggregates data across multiple time steps to train a generative memory and predictor, mitigating catastrophic forgetting by incorporating both past and current data to predict future trajectories of traffic agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a standard trajectory prediction model is used, then the model can process current traffic data, but it suffers from catastrophic forgetting and underutilizes past interaction data
Solution Approach 1:
The system performs preliminary encoding of historical trajectory data into scene graphs before they are needed for prediction. By pre-processing and storing interaction patterns in structured scene graphs, the system preserves past information in a readily usable format that prevents catastrophic forgetting during continual learning
Solution Approach 2:
Scene graphs serve as an intermediary representation between raw historical trajectory data and the prediction model. This intermediate structured format captures semantic relationships and interaction patterns, allowing the model to efficiently utilize past data without being overwhelmed by raw data volume
2Adaptability or versatility
If the model prioritizes recent interactions, then it can adapt to current traffic patterns, but it loses accuracy in predicting trajectories in diverse environments
Solution Approach 1:
The system merges historical scene graphs with current trajectory data to form a comprehensive training dataset. This combination allows the model to learn from both past interaction patterns and current traffic behaviors, achieving continual learning that maintains accuracy across diverse environments while adapting to new patterns
Solution Approach 2:
The system implements continual learning by continuously incorporating new data while preserving and utilizing historical scene graphs. This continuous integration of past and present information ensures the model maintains reliable performance across varying environments rather than forgetting previously learned patterns
3Reliability
If complex road geometries and multiple traffic agents are considered, then prediction completeness improves, but computational complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct components: scene graph construction, interaction pattern extraction, and trajectory prediction. By dividing the problem into manageable stages with specialized processing for each, the system handles complex road geometries and multiple agents without overwhelming computational demands
Data Source
AI summary
A system and method for completing continual multi-agent trajectory forecasting with a graph-based conditional generative memory system that include receiving data associated with a surrounding location of an ego agent and inputting the data associated with the surrounding location of the ego agent to at least one episodic memory buffer and processing scene graphs associated with the surrounding location of the ego agent that are associated with the plurality of time steps. The system and method additionally include aggregating the data associated with the surrounding location of the ego agent associated with the plurality of time steps into mixed data and training a generative memory and a predictor with the mixed data. The system and method further include predicting future trajectories associated with traffic agents that are located within the surrounding location of the ego agent based on the training of the generative memory and the predictor.