首个提供真实车祸3D标注的高速公路数据集，助力自动驾驶安全研究。
Towards Vision Zero: The TUM Traffic Accid3nD Dataset
- 融合规则与学习方法，实现多模态事故检测。
- 含263万2D框、11万帧真实车祸数据，覆盖多种天气光照条件。
- 适合自动驾驶安全、事故分析与感知模型评估的研究者使用。
尽管交通网络安全性研究已取得显著进展，但事故仍频繁发生，可视为交通系统的偶然性结果。目前尚无公开数据集包含从路边摄像头和激光雷达采集的真实车祸3D标注。本文提出TUM Traffic Accid3nD（TUMTraf-Accid3nD）数据集，涵盖不同天气与光照条件下高速公路上的真实车祸场景。数据集包含2,634,233个标注的2D边界框、实例掩码及带轨迹ID的3D边界框，共111,945帧图像与点云数据，由四路路边相机与激光雷达以25 Hz采样。数据集包含六类目标，采用OpenLABEL格式。我们还提出一种结合规则与学习的事故检测模型，实验与消融研究验证了其鲁棒性。数据集、模型与代码已公开于https://accident-dataset.github.io。
原文摘要 · Abstract (English)
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 unavoidable and sporadic outcomes of traffic networks. No public dataset contains 3D annotations of real-world accidents recorded from roadside camera and LiDAR sensors. We present the TUM Traffic Accid3nD (TUMTraf-Accid3nD) dataset, a collection of real-world highway accidents in different weather and lighting conditions. It contains vehicle crashes at high-speed driving with 2,634,233 labeled 2D bounding boxes, instance masks, and 3D bounding boxes with track IDs. In total, the dataset contains 111,945 labeled image and point cloud frames recorded from four roadside cameras and LiDARs at 25 Hz. The dataset contains six object classes and is provided in the OpenLABEL format. We propose 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 website: https://accident-dataset.github.io.
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