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：安全性是自动驾驶的首要目标。然而，当前尚无公开数据集能够直接且可解释地评估自动驾驶的安全性。本研究提出DeepAccident——一个通过高保真模拟器生成的大规模数据集，包含真实驾驶场景中频发的各类事故场景。该数据集包含57K个标注帧和285K个标注样本，其规模约为大规模数据集nuScenes（40K标注样本）的7倍。此外，我们提出一项新任务——端到端运动与事故预测，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，我们设置四辆车和一个基础设施进行数据记录，从而提供事故场景的多视角数据，支持V2X（车联万物）在感知与预测任务中的研究。最终，我们提出基线模型V2XFormer，其运动与事故预测及3D目标检测性能均优于单车模型。