公开1500段行车碰撞预测数据集，助力自动驾驶安全研究
Nexar Dashcam Collision Prediction Dataset and Challenge
- 采集1500段40秒真实路况视频，标注事故与环境信息
- 提供碰撞发生前500至1500毫秒的精准预测时间点
- 支持机器学习模型竞赛，评估提前预警能力
本文发布Nexar行车记录仪碰撞预测数据集及挑战赛，旨在推动交通事件分析与自动驾驶安全研究。数据集包含1500个标注视频片段，每段约40秒，涵盖多样真实交通场景。视频标注了事件类型（碰撞/近碰撞 vs. 正常驾驶）、环境条件（光照、天气）及场景类型（城市、乡村、高速公路等）。对碰撞和近碰撞案例，额外提供精确事件时刻与预警时间标签，标记碰撞首次可预测的时间点。为推进事故预测研究，设立基于该数据集的公开竞赛，要求参赛者构建模型，根据输入视频预测即将发生的碰撞概率。模型性能通过事故前500毫秒、1000毫秒、1500毫秒多个时间窗口的平均精度（AP）评估，强调早期可靠预警的重要性。数据集以开放许可发布，禁止非道德使用，确保研究负责任推进。
原文摘要 · Abstract (English)
This paper presents the Nexar Dashcam Collision Prediction Dataset and Challenge, designed to support research in traffic event analysis, collision prediction, and autonomous vehicle safety. The dataset consists of 1,500 annotated video clips, each approximately 40 seconds long, capturing a diverse range of real-world traffic scenarios. Videos are labeled with event type (collision/near-collision vs. normal driving), environmental conditions (lighting conditions and weather), and scene type (urban, rural, highway, etc.). For collision and near-collision cases, additional temporal labels are provided, including the precise moment of the event and the alert time, marking when the collision first becomes predictable. To advance research on accident prediction, we introduce the Nexar Dashcam Collision Prediction Challenge, a public competition on top of this dataset. Participants are tasked with developing machine learning models that predict the likelihood of an imminent collision, given an input video. Model performance is evaluated using the average precision (AP) computed across multiple intervals before the accident (i.e. 500 ms, 1000 ms, and 1500 ms prior to the event), emphasizing the importance of early and reliable predictions. The dataset is released under an open license with restrictions on unethical use, ensuring responsible research and innovation.
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