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为自动驾驶提供直接且可解释的安全性评估。本研究提出DeepAccident——通过逼真模拟器生成的大规模数据集，涵盖真实驾驶中频繁发生的多样化事故场景。该数据集包含5.7万帧标注数据及28.5万条标注样本，其规模约为大规模数据集nuScenes（含4万条标注样本）的7倍。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。此外，每个场景均设置四辆车辆与一个路侧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）感知与预测任务研究。最后，我们提出基线V2X模型V2XFormer，其在运动与事故预测及三维目标检测任务中相较于单车模型展现出更优性能。