中文标题#
長尾事件的安全關鍵學習：慕尼黑工業大學交通意外數據集
英文标题#
Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
中文摘要#
儘管已經做了大量工作來提高交通網絡的安全性，但事故仍然經常發生。 它們必須被視為交通網絡不可避免且偶發的結果。 我們提出了 TUM 交通事故（TUMTraf-A）數據集，這是一個真實世界高速公路事故的集合。 它包含十段高速行駛中的車輛碰撞序列，包含 294,924 個標記的 2D 框和 93,012 個標記的 3D 框以及在 48,144 個標記幀中的跟踪 ID，這些幀是從四個路邊攝像頭和以 10 Hz 採樣的 LiDAR 記錄的。 該數據集包含十個物體類別，並以 OpenLABEL 格式提供。 我們提出了 Accid3nD，一種將基於規則的方法與基於學習的方法相結合的事故檢測模型。 我們在數據集上的實驗和消融研究顯示了我們所提出方法的魯棒性。 數據集、模型和代碼可在我們的項目網站上獲得：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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