Traffic accident recognition is essential in developing automated driving and Advanced Driving Assistant System technologies.A large dataset of annotated traffic accidents is necessary to improve the accuracy of traffic accident recognition using deep learning models.Conventional traffic accident datasets provide annotations on the presence or absence of traffic accidents and other teacher labels, improving traffic accident recognition performance. Therefore, we propose V-TIDB, a large-scale traffic accident recognition dataset annotated with various environmental information as multi-labels. Our proposed dataset aims to improve the performance of traffic accident recognition by annotating ten types of environmental information in addition to the presence or absence of traffic accidents. V-TIDB is constructed by collecting many videos from the Internet and annotating them with appropriate environmental information.In our experiments, we compare the performance of traffic accident recognition when only labels related to the presence or absence of traffic accidents are trained and when environmental information is added as a multi-label. In the second experiment, we compare the performance of the training with only contact level which represents the severity of the traffic accident, and the performance with environmental information added as a multi-label.The results showed that 6 out of 10 environmental information labels improved the performance of recognizing the presence or absence of traffic accidents. In the experiment on the degree of recognition of traffic accidents, the performance of recognition of car wrecks and contacts was improved for all environmental information. These experiments show that V-TIDB can be used to learn traffic accident recognition models that take environmental information into account in detail and can be used for appropriate traffic accident analysis.

翻译：交通事故识别是自动驾驶与高级驾驶辅助系统技术发展的关键。为提升基于深度学习模型的交通事故识别精度，需要大规模标注的交通事故数据集。传统交通事故数据集仅提供事故存在与否的标注信息及其他教师标签，可提升事故识别性能。为此，我们提出V-TIDB——一个大规模多标签交通事故识别数据集，该数据集标注了多种环境信息作为辅助标签。本数据集在标注事故存在性的基础上，额外标注了十类环境信息，旨在提升事故识别性能。V-TIDB通过从互联网收集大量视频并标注相应环境信息构建而成。实验阶段，我们首先比较了仅使用事故存在性标签训练与引入环境信息作为多标签训练的事故识别性能差异；其次对比了仅使用事故严重程度标签训练与引入环境信息作为多标签训练的性能差异。结果表明，十类环境信息中有六类可提升事故存在性识别性能。在事故程度识别实验中，所有环境信息均提升了车辆碰撞与接触类事故的识别性能。上述实验证明，V-TIDB能够支持对环境影响因素的精细建模，适用于训练考虑环境因素的交通事故识别模型，助力实现合理的交通事故分析。