中文标题#
长尾事件的安全关键学习：慕尼黑工业大学交通意外数据集
英文标题#
Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
中文摘要#
尽管已经做了大量工作来提高交通网络的安全性，但事故仍然经常发生。 它们必须被视为交通网络不可避免且偶发的结果。 我们提出了 TUM 交通事故（TUMTraf-A）数据集，这是一个真实世界高速公路事故的集合。 它包含十段高速行驶中的车辆碰撞序列，包含 294,924 个标记的 2D 框和 93,012 个标记的 3D 框以及在 48,144 个标记帧中的跟踪 ID，这些帧是从四个路边摄像头和以 10 Hz 采样的 LiDAR 记录的。 该数据集包含十个物体类别，并以 OpenLABEL 格式提供。 我们提出了 Accid3nD，一种将基于规则的方法与基于学习的方法相结合的事故检测模型。 我们在数据集上的实验和消融研究显示了我们所提出方法的鲁棒性。 数据集、模型和代码可在我们的项目网站上获得：https://tum-traffic-dataset.github.io/tumtraf-a.
英文摘要#
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as an unavoidable and sporadic outcome of traffic networks. We present the TUM Traffic Accident (TUMTraf-A) dataset, a collection of real-world highway accidents. It contains ten sequences of vehicle crashes at high-speed driving with 294,924 labeled 2D and 93,012 labeled 3D boxes and track IDs within 48,144 labeled frames recorded from four roadside cameras and LiDARs at 10 Hz. The dataset contains ten object classes and is provided in the OpenLABEL format. We propose Accid3nD, an accident detection model that combines a rule-based approach with a learning-based one. Experiments and ablation studies on our dataset show the robustness of our proposed method. The dataset, model, and code are available on our project website: https://tum-traffic-dataset.github.io/tumtraf-a.
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