Accurate prediction of driving intention is key to enhancing the safety and interactive efficiency of human-machine co-driving systems. It serves as a cornerstone for achieving high-level autonomous driving. However, current approaches remain inadequate for accurately modeling the complex spatiotemporal interdependencies and the unpredictable variability of human driving behavior. To address these challenges, we propose CaTFormer, a causal Temporal Transformer that explicitly models causal interactions between driver behavior and environmental context for robust intention prediction. Specifically, CaTFormer introduces a novel Reciprocal Delayed Fusion (RDF) mechanism for precise temporal alignment of interior and exterior feature streams, a Counterfactual Residual Encoding (CRE) module that systematically eliminates spurious correlations to reveal authentic causal dependencies, and an innovative Feature Synthesis Network (FSN) that adaptively synthesizes these purified representations into coherent temporal representations. Experimental results demonstrate that CaTFormer attains state-of-the-art performance on the Brain4Cars dataset. It effectively captures complex causal temporal dependencies and enhances both the accuracy and transparency of driving intention prediction.

翻译：驾驶意图的准确预测是提升人机共驾系统安全性与交互效率的关键，也是实现高级别自动驾驶的基石。然而，现有方法在精确建模复杂的时空相互依赖关系以及人类驾驶行为的不可预测变异性方面仍显不足。为应对这些挑战，我们提出了CaTFormer，一种因果时序Transformer，它显式地对驾驶员行为与环境上下文之间的因果交互进行建模，以实现鲁棒的意图预测。具体而言，CaTFormer引入了一种新颖的互惠延迟融合（RDF）机制，用于精确地对内部与外部特征流进行时序对齐；一个反事实残差编码（CRE）模块，用于系统性地消除虚假相关性以揭示真实的因果依赖关系；以及一个创新的特征合成网络（FSN），用于自适应地将这些净化后的表征合成为连贯的时序表征。实验结果表明，CaTFormer在Brain4Cars数据集上达到了最先进的性能。它有效地捕捉了复杂的因果时序依赖关系，并同时提升了驾驶意图预测的准确性与可解释性。