Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
This work addresses safety-critical accident detection for autonomous driving and traffic monitoring, but is incremental as it builds on existing detection methods with a new dataset.
The authors tackled the problem of detecting traffic accidents, which are rare but critical events, by introducing the TUMTraf-A dataset containing 10 real-world highway crash sequences with 294,924 labeled 2D boxes and 93,012 labeled 3D boxes across 48,144 frames, and proposing Accid3nD, a hybrid rule-based and learning-based detection model that shows robustness in experiments.
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.