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.

发展自动驾驶和高级驾驶辅助系统技术是必要的，而交通事故识别在其中起着重要作用。我们提出了一个大规模交通事故识别数据集 V-TIDB，通过注释多种环境信息作为多标签来提高交通事故识别的性能。实验结果表明 V-TIDB 能够学习细致考虑环境信息的交通事故识别模型，用于适当的交通事故分析。

具有多个标签的交通事故数据库，包含多个视角的环境信息