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Fatigue driving prediction method based on GPS data

A GPS data, fatigue driving technology, applied in the direction of instruments, biological neural network models, neural architectures, etc., can solve problems such as too late, and achieve the effect of ensuring driving safety

Active Publication Date: 2019-03-19
CHANGAN UNIV
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Problems solved by technology

But the disadvantage of real-time monitoring is that it does not give the driver time to react in advance, that is, once fatigue is detected, it may be too late for the driver to take action

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  • Fatigue driving prediction method based on GPS data
  • Fatigue driving prediction method based on GPS data
  • Fatigue driving prediction method based on GPS data

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Embodiment Construction

[0036] The present invention is described in further detail below in conjunction with accompanying drawing:

[0037] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless defined as herein Explanation.

[0038] The realization of the fatigue driving prediction method based on GPS data of the present invention mainly comprises the following steps:

[0039] Step 1, build GPS database:

[0040] The one-year vehicle driving data collected by the vehicle-mounted GPS device is used as the GPS database. The driving data include...

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Abstract

The invention discloses a fatigue driving prediction method based on GPS data. The fatigue driving prediction method comprises the following steps that 1, a GPS database is built; 2, driving data is selected in the GPS database, the driving data is set as a plurality of prediction variables related with fatigue driving, and the prediction variables are standardized to serve as samples; a fatigue driving situation corresponds to each sample, and the samples are divided into the samples which belong to the fatigue driving state and the samples which do not belong to the fatigue driving state; the samples are divided into a training set and a verification set in a proportion of 7:3; 3, a multi-layer perception neural network including an input layer, a hidden layer and an output layer is built, and a classification model used for predicting fatigue driving is generated; 4, whether the fatigue driving situation can occur or not during the prediction period is predicted by using the model,and when the fatigue driving situation can occur during the prediction period, a driver is reminded. By means of the fatigue driving prediction method, whether the fatigue driving situation occurs ornot can be predicted in advance, and the driver is early warned.

Description

technical field [0001] The invention belongs to the field of road safety and relates to a fatigue driving prediction method based on GPS data. Background technique [0002] According to the analysis of traffic accident causes, fatigue driving is one of the main causes of fatal traffic accidents. According to statistics, in the major traffic accidents caused by fatigue driving, the proportion is as high as 40%. It can be seen that the harm caused by fatigue driving is very serious, which has attracted great attention from governments of all countries. In order to reduce accidents caused by fatigue driving, fatigue driving detection technology has been developed by various countries. The detection method based on the driver's physiological characteristics is to judge the driver's fatigue state according to the driver's physiological indicators (such as EEG, EOG, etc.). Interference is not conducive to safe driving. Most of the existing fatigue driving early warnings are rea...

Claims

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Application Information

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IPC IPC(8): G08B21/06G06N3/04
CPCG06N3/04G08B21/06
Inventor 牛世峰李贵强郑佳红王青青刘彦君
Owner CHANGAN UNIV
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