Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for detecting risk level of driver based on hidden Markov model

A hidden Markov and risk level technology, applied in the level field, can solve the problems of less research on driver risk level identification and cannot meet traffic safety management, so as to reduce casualties and property losses, improve overall safety, and improve safety effect

Active Publication Date: 2020-01-17
CHANGAN UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the existing achievements are mainly on the evaluation of driver risk level, and there are few studies on driver risk level identification, which cannot meet the requirements of traffic safety management.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for detecting risk level of driver based on hidden Markov model
  • Method for detecting risk level of driver based on hidden Markov model
  • Method for detecting risk level of driver based on hidden Markov model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] The present invention first analyzes the speed data of each driver, designs the acceleration index, and utilizes the characteristic index to identify the driver's driving behavior sequence. Then, using the obtained alarm type of each driver, the K-Means mean clustering method is used to divide the drivers into low-risk drivers, medium-risk drivers and high-risk drivers. The classification of driver risk level is the basis of driver risk level identification, and whether the classification is reasonable or not directly determines the success or failure of the identification algorithm. After using part of the data to train the hidden Markov model, the driving behavior sequence is recognized.

[0036] Among them, driving behaviors include fast deceleration, slow deceleration, normal driving, slow acceleration, and fast acceleration.

[0037] 1. Calculation of veh...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for detecting the risk level of a driver based on a hidden Markov model. By collecting the speed data of the vehicle and different alarm types of the driver, the driving behaviors are judged, the risk levels of the driver are classified, the trained hidden Markov model is used for recognizing the risk levels of the driver, and then the recognition result is used for judging the risk levels of the driver. The method is mainly used for a transportation enterprise safety management system, and when it is identified that the driver is a high-risk-level driver, corresponding management training measures can be taken to improve the safety of the transportation enterprise. The practicability of the method can reduce casualties and property loss caused by traffic accidents, and the overall safety of a traffic system is improved.

Description

technical field [0001] The invention belongs to the field of traffic safety, and in particular relates to a method for detecting a driver's risk level based on a hidden Markov model. Background technique [0002] With the rapid development of the national economy and the acceleration of the urbanization process, the number of motor vehicles and road traffic in my country has increased rapidly, and the problem of traffic accidents has become increasingly prominent. Existing studies have shown that driver factors are the main cause of traffic accidents. Drivers with different driving risk levels have different contributions to traffic accidents. Drivers with low risk levels may cause fewer or even avoid traffic accidents, while drivers with low risk levels may cause fewer or even avoid traffic accidents, while Traffic accidents caused by drivers with higher risk levels may be more serious. Therefore, it is particularly important to conduct in-depth research on the identificat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/24G06F18/295
Inventor 牛世峰董兆晨郑佳红付锐郭应时袁伟
Owner CHANGAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com