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 contains 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, based on the proposed dataset, 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支持自动驾驶的直接且可解释的安全性评估。本文提出DeepAccident，这是一个通过真实感模拟器生成的大规模数据集，包含真实驾驶中频繁发生的多样化事故场景。所提出的DeepAccident数据集包含5.7万帧标注数据和28.5万个标注样本，其规模约为大规模数据集nuScenes（含4万个标注样本）的7倍。此外，基于该数据集，我们提出一项新任务——端到端运动与事故预测，可用于直接评估不同自动驾驶算法的事故预测能力。进一步地，针对每个场景，我们设置四辆车辆和一个基础设施记录数据，从而提供事故场景的多视角数据，并支持感知与预测任务的V2X（车联万物）研究。最后，我们提出名为V2XFormer的基线V2X模型，该模型在运动与事故预测以及三维目标检测任务上展现出优于单车辆模型的性能。