AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction
CoRR(2023)
摘要
Multi-agent trajectory prediction, as a critical task in modeling complex
interactions of objects in dynamic systems, has attracted significant research
attention in recent years. Despite the promising advances, existing studies all
follow the assumption that data distribution observed during model learning
matches that encountered in real-world deployments. However, this assumption
often does not hold in practice, as inherent distribution shifts might exist in
the mobility patterns for deployment environments, thus leading to poor domain
generalization and performance degradation. Consequently, it is appealing to
leverage trajectories from multiple source domains to mitigate such
discrepancies for multi-agent trajectory prediction task. However, the
development of multi-source domain generalization in this task presents two
notable issues: (1) negative transfer; (2) inadequate modeling for external
factors. To address these issues, we propose a new causal formulation to
explicitly model four types of features: domain-invariant and domain-specific
features for both the focal agent and neighboring agents. Building upon the new
formulation, we propose AdapTraj, a multi-source domain generalization
framework specifically tailored for multi-agent trajectory prediction. AdapTraj
serves as a plug-and-play module that is adaptable to a variety of models.
Extensive experiments on four datasets with different domains demonstrate that
AdapTraj consistently outperforms other baselines by a substantial margin.
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