交通机构现在可以访问大量视频数据来研究安全和拥堵情况。这些数据大部分是观察性的，是在没有干预的情况下收集的,，这使得诸如"降雨如何改变交通密度?"之类的因果问题很难回答。我们提出了 teLLMe, 一个用于城市驾驶数据集探索性因果分析的系统。该系统从根据行车记录仪注释构建的结构化事件表开始，并将因果结构学习与 PC 算法, 基于引导的稳定性检查, 以及使用线性回归和 DoWhy 的查询特定效果估计相结合。通过模式感知 LLM, 将自然语言问题映射到结构化因果查询，使用户能够指定治疗, 结果, 和亚群。 teLLMe 返回"Causal Card"，其中总结了效果估计, 调整集, DAG 支持, 和假设,，后面是简短的自然语言解释。 BDD 派生的交通事件的案例研究表明，该系统可以呈现涉及天气, 高峰时间, 和交通密度, 的合理关系，同时明确不确定性和建模选择。该系统被设计为假设生成和专家推理的工具，而不是明确因果关系的来源。
Traffic agencies now have access to large volumes of video-derived data for studying safety and congestion. Most of these data are observational and collected without interventions, which makes causal questions such as "How would rain change traffic density?" difficult to answer. We present teLLMe, a system for exploratory causal analysis of urban driving datasets. The system starts from a structured event table built from dashcam annotations and combines causal structure learning with the PC algorithm, bootstrap-based stability checks, and query-specific effect estimation using linear regression and DoWhy. Natural-language questions are mapped to structured causal queries through a schema-aware LLM, enabling users to specify treatments, outcomes, and subpopulations. teLLMe returns a "Causal Card" that summarizes effect estimates, adjustment sets, DAG support, and assumptions, followed by a short natural-language explanation. Case studies on BDD-derived traffic events show that the system can surface plausible relationships involving weather, peak hours, and traffic density, while making uncertainty and modeling choices explicit. The system is designed as a tool for hypothesis generation and expert reasoning rather than a source of definitive causal claims.
科目:人工智能(cs.AI);人机交互(cs.HC)
Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)