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.

中文翻译：

---

通过反事实分析进行人体轨迹预测

在复杂动态环境中预测人类轨迹在自动驾驶汽车和智能机器人中起着至关重要的作用。大多数现有方法学习通过来自历史轨迹的行为线索和来自环境的交互线索来预测未来的轨迹。但是，忽略了训练和部署环境之间的固有偏差。因此，我们提出了一种人类轨迹预测的反事实分析方法，以研究预测轨迹与输入线索之间的因果关系，并减轻环境偏差带来的负面影响。我们首先使用历史轨迹、未来轨迹和环境相互作用构建轨迹预测的因果图。然后，我们通过构建对轨迹本身的反事实干预来切断从环境到轨迹的推断。最后，我们比较事实和反事实轨迹线索，以减轻环境偏差的影响并突出轨迹线索。我们的反事实分析是一个即插即用模块，可应用于任何基线预测方法，包括基于 RNN 和 CNN 的方法。我们表明，我们的方法对不同的基线实现了一致的改进，并在公共行人轨迹预测基准上获得了最先进的结果。