Continual Multi-Agent Interaction Behavior Prediction With Conditional Generative Memory

Multi-agent trajectory prediction plays a crucial role in robotics and autonomous driving. The current mainstream research focuses on how to achieve accurate prediction on one large dataset. However, whether the multi-agent trajectory prediction model can be trained with a sequence of datasets, i.e., continual learning settings, remains a question. Can the current prediction methods avoid catastrophic forgetting? Can we utilize the continual learning strategy in the multi-agent trajectory prediction application? Motivated by the generative replay methods in continual learning literature, we propose a multi-agent interaction behavior prediction framework with a graph-neural-network-based conditional generative memory system to mitigate catastrophic forgetting. To the best of our knowledge, this work is the first attempt to study the continual learning problem in multi-agent interaction behavior prediction problems. We empirically show that several approaches in literature indeed suffer from catastrophic forgetting, and our approach succeeds in maintaining a low prediction error when datasets come in a sequential way. We also conduct an ablation analysis to show the effectiveness of our proposed approach.