Lane-change prediction is a central task in intelligent vehicles, where early maneuver anticipation can support safer decision-making. However, many existing approaches mainly learn statistical associations between observed driving variables and future maneuvers, while overlooking the causal dependencies among the input variables themselves. This limits interpretability, especially when physically related variables such as longitudinal gap, relative longitudinal velocity, and Time-To-Collision (TTC) are treated as independent flat inputs. This article presents a causal-inference-based framework for lane-change prediction and explanation. The proposed approach combines linguistic feature construction, expert-constrained causal discovery, deep structural causal modeling with Deep End-to-end Causal Inference (DECI), intervention-based effect analysis, refutation testing, and recursive causal-chain explanation. The objective is not only to predict the future maneuver, but also to identify candidate variables that directly contribute to the prediction, the upstream factors influencing them, and the causal chains through which these effects propagate. The framework achieves average F1-scores above 95% during the first three seconds before the lane-marking crossing event. Beyond prediction accuracy, the framework uses intervention-based effect analysis to distinguish influential from weakly influential variables under the learned causal structure. It further distinguishes candidate direct contributors from mediated effects and generates contrastive causal-chain explanations that clarify why the predicted maneuver is favored and why the alternative maneuvers are less supported. The main contribution is therefore a mechanism-aware lane-change prediction pipeline that moves beyond correlation-based classification toward more interpretable causal reasoning for maneuver prediction.

翻译：换道预测是智能车辆的核心任务之一，提前预判驾驶员操作能为安全决策提供支持。然而，现有方法主要学习观测驾驶变量与未来操作之间的统计关联，却忽视了输入变量间的因果依赖关系。当纵向间距、相对纵向速度和碰撞时间（TTC）等物理相关变量被当作独立平面输入处理时，这种局限性会削弱模型的可解释性。本文提出基于因果推断的换道预测与解释框架。该方法融合了语言特征构建、专家约束的因果发现、基于深度端到端因果推断（DECI）的深度结构因果建模、干预效应分析、反证检验及递归因果链解释。研究目标不仅是预测未来操作，更要识别直接贡献于预测的关键候选变量、影响这些变量的上游因素，以及效应传播的因果链。该框架在车道线穿越事件发生前三秒内，平均F1分数超过95%。除预测精度外，框架通过干预效应分析，在学习到的因果结构下区分强影响变量与弱影响变量，进一步甄别直接贡献者和中介效应，并生成对比性因果链解释，阐明为何预测操作更受支持而替代操作缺乏依据。本文主要贡献在于构建了机制感知的换道预测流水线，推动操作预测从基于关联的分类向更具可解释性的因果推理演进。