On-board diagnosis system data-based traffic condition analysis and early warning method

Abstract

The invention relates to an on-board diagnosis system data-based traffic condition analysis and early warning method. According to the method, OBD (on-board diagnosis) sample data installed on a vehicle are read, so that vehicle traveling data can be obtained, and abnormal behaviors are monitored; the abnormal behaviors are uploaded to a cloud data analysis center, the driving behaviors of a userare analyzed and learned through using various deep learning algorithms; modeling and evaluation are performed on the sample data a plurality of number of times, and therefore, the congestion condition of a current road can be judged, whether dangerous behaviors such as sudden braking will easily appear can be judged; and after enough data are obtained and analyzed, a system performs secondary modeling according to the driving behaviors of the user and informs the user of a current traffic condition. According to the method of the present invention, a field traffic condition collection processis subtly avoided; a traffic condition is estimated, the consumption of a lot of money to collect traffic condition information is avoided, and therefore, the deployment difficulty of a project is deceased, and capital consumption can be decreased. With the method adopted, an intelligent analysis and reminding function can be realized; bad driving behaviors can be avoided to a certain extent; congested roads can be avoided; and drivers can be warned against sections where accidents happen frequently.

Description

technical field [0001] The invention belongs to the technical field of Internet of Vehicles security, and relates to a road condition analysis and early warning method based on vehicle diagnostic system data. Background technique [0002] With the rapid development of economy and society, traffic elements such as people, vehicles, and roads have increased sharply, and traffic problems across the country have become the focus. Existing driving behavior prediction technologies simply analyze driving time and driving mileage. They only collect data such as driving speed, driving time, driving mileage, and sudden braking times, and then refer to fixed standards to make judgments. The degree of intelligence is very low. It cannot be intelligently reminded according to different users and different road sections. It only relies on the auxiliary functions of the air conditioner and the window switch system, so it cannot be widely used in various vehicle models and cannot meet the ...

AI Technical Summary

Problems solved by technology

The existing driving behavior prediction technology simply analyzes the driving time and driving mileage, only collects data such as driving speed, driving time, driving mileage, and the number of sudden brakes, and then makes judgments with reference to fixed standards, which is very low in intelligence

It is not possible to provide intelligent reminders according to different users and different road sections

It only relies on the auxiliary functions of the air conditioner and window switch system, so it cannot be widely used in various models and cannot meet the needs of users

Moreover, most of the existing technologies on the market are mechanical safety reminders, and do not have intelligent functions to deal with emergencies, etc.

The degree of intelligence is low. It can only judge the road conditions and evaluate the driving status according to fixed standards. The scope of application is narrow and it cannot respond to the needs of the era for the Internet of Vehicles.

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