Due to the increase in motor vehicle accidents, there is a growing need for high-performance car crash detection systems. The authors of this research propose a car crash detection system that uses both video data and audio data from dashboard cameras in order to improve car crash detection performance. While most existing car crash detection systems depend on single modal data (i.e., video data or audio data only), the proposed car crash detection system uses an ensemble deep learning model based on multimodal data (i.e., both video and audio data), because different types of data extracted from one information source (e.g., dashboard cameras) can be regarded as different views of the same source. These different views complement one another and improve detection performance, because one view may have information that the other view does not contain. In this research, deep learning techniques, gated recurrent unit (GRU) and convolutional neural network (CNN), are used to develop a car crash detection system. A weighted average ensemble is used as an ensemble technique. The proposed car crash detection system, which is based on multiple classifiers that use both video and audio data from dashboard cameras, is validated using a comparison with single classifiers that use video data or audio data only. Car accident YouTube clips are used to validate this research. The experimental results indicate that the proposed car crash detection system performs significantly better than single classifiers. It is expected that the proposed car crash detection system can be used as part of an emergency road call service that recognizes traffic accidents automatically and allows immediate rescue after transmission to emergency recovery agencies.

由于机动车事故的增加，对高性能汽车碰撞检测系统的需求不断增长。本研究的作者提出了一种车祸检测系统，该系统使用来自仪表板摄像头的视频数据和音频数据，以提高车祸检测性能。虽然大多数现有的车祸检测系统依赖于单模态数据（即仅视频数据或音频数据），但所提出的车祸检测系统使用基于多模态数据（即视频和音频数据）的集成深度学习模型，因为从一个信息源（例如仪表盘摄像头）中提取的不同类型的数据可以被视为同一信息源的不同视图。这些不同的视图相互补充并提高检测性能，因为一个视图可能包含另一个视图不包含的信息。在这项研究中，深度学习技术、门控循环单元 (GRU) 和卷积神经网络 (CNN) 被用于开发车祸检测系统。加权平均集成被用作集成技术。所提出的汽车碰撞检测系统基于使用来自仪表板摄像头的视频和音频数据的多个分类器，通过与仅使用视频数据或音频数据的单个分类器进行比较来验证。车祸 YouTube 剪辑用于验证这项研究。实验结果表明，所提出的汽车碰撞检测系统的性能明显优于单一分类器。