  Trajectory prediction is critical for autonomous driving vehicles. Most
existing methods tend to model the correlation between history trajectory
(input) and future trajectory (output). Since correlation is just a superficial
description of reality, these methods rely heavily on the i.i.d. assumption and
evince a heightened susceptibility to out-of-distribution data. To address this
problem, we propose an Out-of- Distribution Causal Graph (OOD-CG), which
explicitly defines the underlying causal structure of the data with three
entangled latent features: 1) domain-invariant causal feature (IC), 2)
domain-variant causal feature (VC), and 3) domain-variant non-causal feature
(VN ). While these features are confounded by confounder (C) and domain
selector (D). 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.
We evaluate the performance of CILF in different vehicle trajectory prediction
models on the mainstream datasets NGSIM and INTERACTION. Experiments show
promising improvements in CILF on domain generalization.
