Existing methods for trajectory prediction predominantly employ scene fusion to enhance model performance. However, they fail to provide a rational explanation as to why the fusion of the scene context and trajectories improves model performance, which prevents them from identifying the fundamental factors limiting model performance. Hence, this paper introduces a Structured Causal Model for trajectory prediction based on causal inference, which elucidates the genuine reasons for the performance enhancement brought about by the scene context in trajectory prediction and analyzes the confounding path interference that curtails model performance. Specifically, this paper first employs the front-door criterion to eliminate the confounders during the feature extraction process, allowing the model to fairly incorporate the scene context into the spatio-temporal state. Subsequently, a spatio-temporal causal graph is generated to further extract the causal relationship of the trajectory in the current scene, serving as the spatio-temporal representation. Finally, the technique of counterfactual representation inference extrapolates the spatio-temporal features of the historical trajectory into future traffic scenes for trajectory prediction. The effectiveness and reliability of this proposed end-to-end method has been experimentally validated on two real-world datasets in real traffic scenarios, particularly in scenarios involving interactions between multiple agents.

中文翻译：

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基于时空结构因果模型的场景融合可靠轨迹预测

现有的轨迹预测方法主要采用场景融合来增强模型性能。然而，他们未能对为什么场景上下文和轨迹的融合能够提高模型性能提供合理的解释，这使得他们无法识别限制模型性能的基本因素。因此，本文提出了一种基于因果推理的轨迹预测结构化因果模型，阐明了场景上下文在轨迹预测中带来性能提升的真正原因，并分析了影响模型性能的混杂路径干扰。具体来说，本文首先采用前门准则来消除特征提取过程中的混杂因素，使模型能够公平地将场景上下文融入到时空状态中。随后，生成时空因果图，进一步提取当前场景中轨迹的因果关系，作为时空表示。最后，反事实表示推理技术将历史轨迹的时空特征外推到未来的交通场景中以进行轨迹预测。这种端到端方法的有效性和可靠性已经在真实交通场景中的两个真实数据集上得到了实验验证，特别是在涉及多个代理之间交互的场景中。