  Forecasting human trajectories in complex dynamic environments plays a
critical role in autonomous vehicles and intelligent robots. Most existing
methods learn to predict future trajectories by behavior clues from history
trajectories and interaction clues from environments. However, the inherent
bias between training and deployment environments is ignored. Hence, we propose
a counterfactual analysis method for human trajectory prediction to investigate
the causality between the predicted trajectories and input clues and alleviate
the negative effects brought by environment bias. We first build a causal graph
for trajectory forecasting with history trajectory, future trajectory, and the
environment interactions. Then, we cut off the inference from environment to
trajectory by constructing the counterfactual intervention on the trajectory
itself. Finally, we compare the factual and counterfactual trajectory clues to
alleviate the effects of environment bias and highlight the trajectory clues.
Our counterfactual analysis is a plug-and-play module that can be applied to
any baseline prediction methods including RNN- and CNN-based ones. We show that
our method achieves consistent improvement for different baselines and obtains
the state-of-the-art results on public pedestrian trajectory forecasting
benchmarks.
