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.

本文提出了 DeepAccident 数据集，据此可以直接评估不同自动驾驶算法的事故预测能力，这个数据集包含了多种真实世界中常见的事故场景，并提供了可用于感知和预测任务的多视角数据，并展示了基于 V2X 的运动和事故预测任务及 3D 物体检测的基准模型 V2XFormer。