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Driver injury grade prediction algorithm

A prediction algorithm and driver technology, applied in the field of traffic safety and rescue, can solve problems such as low versatility, complex algorithms, and large differences in models, and achieve the effect of simple data processing methods, accurate data sources, and high accuracy.

Inactive Publication Date: 2019-02-15
JIANGSU UNIV
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

However, the Logistic regression model algorithm is relatively complex, and the models obtained based on different samples are quite different, and the versatility is not high.
In summary, the current algorithm for driver injury prediction is complex and difficult to operate

Method used

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  • Driver injury grade prediction algorithm
  • Driver injury grade prediction algorithm

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Embodiment Construction

[0036] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0037] Such as figure 1 Shown, the present invention passes through the following steps:

[0038] Step 1, through the analysis of the influencing factors that cause the driver's injury level, collect the driver's injury level and influencing factor data to form a training sample data set; and input the relevant data into the data table. Among them, the driver's injury level is based on whether it reaches MAIS3+ (Maximum Abbreviated Injury Scale) as an evaluation index; the influencing factors include vehicle speed change, collision direction, driver's age, whether the driver wears a seat belt, and...

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Abstract

The invention discloses a driver injury grade prediction algorithm, which collects driver injury grade and influencing factor data to form a training sample data set. The training sample data set is divided into learning data and to-be-tested data, and the learning data and to-be-tested data are discretized to obtain standardized learning data and to-be-tested data. According to the standardized learning data, the naive Bayesian network structure diagram of driver injury grade is established. The conditional probabilities of each factor under different injury grades and the prior probabilitiesunder different injury grades are obtained by calculating the normalized learning data with simulation software, and then the driver injury prediction algorithm based on Naive Bayesian Model is constructed. The driver injury prediction algorithm based on naive Bayesian model is constructed by inputting normalized data to be tested, and the prediction results are verified, which can provide a simpler and more effective data processing method and improve the accuracy of the prediction algorithm.

Description

technical field [0001] The invention belongs to the technical field of traffic safety rescue, and in particular relates to a driver injury level prediction algorithm. Background technique [0002] With the increase of the number of cars in China, how to predict the driver's injury level in the car collision becomes more and more important, so it is necessary to study the driver's injury prediction algorithm. At present, a complete set of driver injury prediction system has been proposed abroad, which is called advanced automatic emergency call system for vehicle accidents. [0003] The Advanced Automatic Crash Notification (AACN) system can provide the precise location of the accident scene and predict the severity of the driver's injury in the event of a vehicle collision, and then the on-board recorder will record the relevant data. If the driver is seriously injured, AACN will call for help and send the driver's injury data to the medical center, and the medical center w...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q10/04
CPCG06Q10/04G06F18/29G06F18/214
Inventor 陆颖殷越洲
Owner JIANGSU UNIV
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