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Found 2 papers, 1 papers with code
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction
To leverage causal features for prediction, we propose a Causal Inspired Learning Framework (CILF), which includes three steps: 1) extracting domain-invariant causal feature by means of an invariance loss, 2) extracting domain variant feature by domain contrastive learning, and 3) separating domain-variant causal and non-causal feature by encouraging causal sufficiency.
Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting
HMNet first infers the hierarchical difference on motions to encode physically compliant patterns with high expressivity of moving trends and driving intentions.
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