An automatic highway risk assessment method based on feature construction and fusion

A technology for automatic assessment and highway, applied in the direction of neural architecture, road vehicle traffic control system, instrument, etc., can solve the problem of lack of objective assessment methods for highway safety risk assessment

Active Publication Date: 2020-09-25
RES INST OF HIGHWAY MINIST OF TRANSPORT
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Problems solved by technology

[0006] The purpose of the present invention is to realize the automatic assessment of highway risk based on feature construction and fusion, thereby solving the problem of lacking an effective objective assessment method based on environmental factors in the existing highway safety risk assessment

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  • An automatic highway risk assessment method based on feature construction and fusion
  • An automatic highway risk assessment method based on feature construction and fusion
  • An automatic highway risk assessment method based on feature construction and fusion

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Embodiment Construction

[0045] The preferred embodiments of the present invention will be further described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention.

[0046] The present invention is based on the road condition data of the known risk level, uses the deep neural network to model, extracts the statistical relationship between the safety facility index and the risk level of the road section through the machine learning algorithm, and then uses the model that embodies the statistical relationship to realize Analyze the road condition data of the unknown risk level to evaluate the risk level of the corresponding road section.

[0047]In order to meet the modeling requirements of the deep neural network, it is necessary to extract the road condition parameters from the road condition data with known risk levels, correct them as needed, and transform them into a data form that can be recognized by the computer.

[0048...

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Abstract

The invention discloses an automatic road risk assessment method. By collecting the road condition parameters of the highway and transforming the road condition parameters into a data form recognizable by a computer, the data is analyzed by using a multi-layer fusion deep network, and a road condition parameter reflecting road safety parameters is established. The deep neural network model of the relationship between risk levels is used to evaluate the safety risk of highways with unknown risk levels. Through the automatic highway risk assessment method of the present invention, various highway environment parameters can be analyzed and integrated by means of statistical learning, the relationship between highway risk levels and road condition parameters can be effectively extracted, and then the highway risk level can be automatically assessed.

Description

technical field [0001] The invention mainly relates to highway risk assessment, in particular to an automatic highway risk assessment method using intelligent information processing technology and based on feature construction and fusion. Background technique [0002] Highway risk assessment is an important research topic in the field of traffic safety. Accurate positioning of dangerous road sections based on the assessment of the risk level of specific road sections can guide the key direction of highway construction, discover dangerous road sections in time, and effectively avoid traffic accidents. [0003] In the traditional highway planning and design, the risk level assessment is mostly carried out manually, and the assessment model is designed based on experience and theory. Although the evaluation results of some models are credible after being verified by practice, due to the limitations of human experience, computing power, and models used, the number of road chara...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06N3/06G06N3/04
CPCG06N3/061G08G1/0129G06N3/044G06N3/045
Inventor 张潇丹陈永胜黄程韦
Owner RES INST OF HIGHWAY MINIST OF TRANSPORT
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