构建真实事故视频数据集，挑战自动驾驶因果推理能力。
CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios
- 基于2249段行车记录仪视频，标注事故责任与规则违例
- 模型对光照识别较好，但天气与道路状况判断低于基线
- 揭示视觉语言模型在事故责任推理上的严重短板
尽管现代自动驾驶系统在目标检测和轨迹预测等感知任务上表现优异，但在解释交通事故所需的高层级因果推理方面仍显不足，尤其在确定责任归属（如谁应负责、违反了哪项交通规则）方面，现有基准尚未充分探索。为此，我们提出CAViAR（Causal Accident Video and Incident Analysis Repository），一个由人工标注的行车记录仪基准数据集，包含从CarCrashDataset（CCD）和Nexar收集的2,249段真实世界事故视频。每段视频均带有结构化标签，涵盖环境条件、事故类型、因果解释、明显责任方、受影响方及明显规则违例类别。我们对当前先进的视觉-语言模型（VLMs），包括Cosmos-Reason2、Qwen3-VL和InternVL3进行了基准测试。在考虑类别不平衡并采用平衡评估指标后发现，感知能力不均衡——光照识别接近完成，而天气与道路状况准确率仅达或低于多数类基线；所有模型在事故类型与责任推理上均出现显著下降。总体而言，CAViAR揭示了一个实际的感知-推理鸿沟：当前VLM可能识别显著上下文，却无法可靠地将可见行为映射到安全关键驾驶场景中的规则相关责任类别。代码、标注规范、提示模板与评估脚本已公开于：https://github.com/nec-labs-ma/CAViAR
原文摘要 · Abstract (English)
While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpret traffic accidents. In particular, determining responsibility, such as identifying who is at fault and which traffic rule was violated, remains largely unexplored in current benchmarks. To this end, we introduce CAViAR (Causal Accident Video and Incident Analysis Repository), a human-annotated dashcam benchmark comprising 2,249 real-world accident videos collected from CarCrashDataset (CCD) and Nexar. Each video is annotated with structured labels spanning environmental conditions, accident type, causal explanation, apparent At-Fault Agent, affected agent, and apparent rule-violation category. We benchmark state-of-the-art vision-language models (VLMs), including Cosmos-Reason2, Qwen3-VL, and InternVL3. Once class imbalance is accounted for with majority/random baselines and balanced metrics, perceptual competence is uneven--lighting is nearly solved, whereas weather and road-condition accuracy fall at or below the majority-class baseline---and all models degrade sharply on accident type and responsibility reasoning. Overall, CAViAR exposes a practical Perception--Reasoning Gap: current VLMs may recognize salient context, but do not reliably map visible agent actions to annotated rule-relevant responsibility categories in safety-critical driving scenarios. Code, annotation schema, prompts, and evaluation scripts are available at: https://github.com/nec-labs-ma/CAViAR
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