Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and accident prediction, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms. Furthermore, for each scenario, we set four vehicles along with one infrastructure to record data, thus providing diverse viewpoints for accident scenarios and enabling V2X (vehicle-to-everything) research on perception and prediction tasks. Finally, we present a baseline V2X model named V2XFormer that demonstrates superior performance for motion and accident prediction and 3D object detection compared to the single-vehicle model.

翻译：安全是自动驾驶的首要目标。然而，目前尚无公开数据集能够直接且可解释地评估自动驾驶安全性。本研究提出DeepAccident——一个通过高保真仿真器生成的大规模数据集，包含真实驾驶中频发、多样化的交通事故场景。该数据集包含5.7万帧标注图像及28.5万个标注样本，其规模约为大规模数据集nuScenes（4万个标注样本）的7倍。此外，我们提出了一项新任务——端到端运动与事故预测，该任务可直接评估不同自动驾驶算法的事故预测能力。针对每个场景，我们部署了四辆汽车与一个路侧基础设施进行数据采集，从而提供事故场景的多视角数据，并支持V2X（车联万物）感知与预测任务研究。最终，我们提出名为V2XFormer的基线模型，其在运动预测、事故预测及三维目标检测任务上均优于单车模型。