CAViAR：用于真实场景细粒度事故推理的因果视频数据集
原标题：CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios
AI 摘要
CAViAR 是一个由 NEC 实验室发布的新型因果视频数据集，包含 2,249 个真实行车记录仪事故视频，并标注了环境条件、事故类型、因果解释、责任方和违规规则等信息。通过评估多个视觉语言模型，研究发现现有模型在感知任务上表现较好，但在事故类型和责任推理上存在明显不足，揭示了感知与推理之间的差距。该数据集和代码已公开，旨在推动自动驾驶安全领域的多模态推理研究。
正文节选
CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios Abstract 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