Regional traffic accident early warning method and system based on deep learning

A technology of traffic accidents and deep learning, applied in the field of traffic safety, can solve the problem of not reducing safety accidents to a large extent, and achieve the effect of avoiding regional traffic accidents and accurate warning prompts

Inactive Publication Date: 2022-03-25
武汉卓尔信息科技有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] In view of this, the embodiment of the present invention provides a regional traffic accident early warning method and system based on deep learning to solve the problem that the existing early warning methods do not reduce safety accidents to a large extent

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  • Regional traffic accident early warning method and system based on deep learning
  • Regional traffic accident early warning method and system based on deep learning
  • Regional traffic accident early warning method and system based on deep learning

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Embodiment Construction

[0020] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following The described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0021] The term "comprising" and other expressions with similar meanings in the description or claims of the present invention and the above drawings mean that the coverage is not exclusive, such as the process, method or system, equipment comprising a series of steps or units are not limited to Steps or units listed. In addition, "first...

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Abstract

The invention provides a regional traffic accident early warning method and system based on deep learning, and the method comprises the steps: obtaining traffic accident occurrence places in a historical period of time, carrying out the clustering of the traffic accident occurrence places based on a DBSCAN clustering algorithm, and obtaining a regional traffic accident point cluster; carrying out feature extraction on regional traffic accidents, constructing a mapping relation between traffic accident features and accident types, and training a deep learning model based on a deep learning algorithm; and detecting regional traffic characteristic data, predicting a vehicle accident risk through a deep learning model, and carrying out early warning on a corresponding vehicle. According to the scheme, the regional vehicle risk can be predicted, risk prompting can be accurately carried out on regional drivers, and traffic safety is guaranteed.

Description

technical field [0001] The invention relates to the field of traffic safety, in particular to a method and system for early warning of regional traffic accidents based on deep learning. Background technique [0002] Generally, on roads where traffic accidents often occur, the vehicle's map navigation system will usually give voice reminders. At the same time, it will also give reminders for some extreme weather or traffic jams. However, it is usually difficult to arouse the vigilance of the driver for this common prompting method, and the occurrence of safety accidents has not been substantially reduced to a large extent. Contents of the invention [0003] In view of this, an embodiment of the present invention provides a deep learning-based regional traffic accident early warning method and system to solve the problem that the existing early warning methods do not reduce safety accidents to a large extent. [0004] In the first aspect of the embodiments of the present in...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/26G06K9/62G06N3/04G06N3/08G08G1/01G08G1/16G08B31/00
CPCG06Q10/04G06Q10/0635G06Q50/265G06N3/08G08G1/0125G08G1/0129G08G1/0137G08G1/16G08B31/00G06N3/048G06N3/045G06F18/2321
Inventor 周显敬刘虎汪寒雨黄银地
Owner 武汉卓尔信息科技有限公司
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