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.
翻译:在复杂的动态环境中预测人类轨迹在自主飞行器和智能机器人中发挥着关键作用。大多数现有方法都通过历史轨迹和环境互动线索中的行为线索来预测未来轨迹。然而,培训和部署环境之间的固有偏差被忽视。因此,我们提议了一种反事实分析方法,用于人类轨迹预测,以调查预测轨迹和输入线索之间的因果关系,并减轻环境偏差带来的消极影响。我们首先用历史轨迹预测、未来轨迹和环境相互作用建立一个因果图表。然后,我们通过在轨迹本身上建立反事实干预,从环境到轨迹。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插曲模块,可用于任何基线预测方法,包括RNN和CNN的模型。我们显示,我们的方法在不同的基线上取得了一致的改进,并在公共行道轨迹预测基准上获得了最新结果。