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

Driving intention identification method based on improved HMM and SVM double-layer algorithm

A driving intention and recognition method technology, applied in character and pattern recognition, computing, computer parts, etc., can solve the problems of less classification of driving intentions, no consideration of driver information, and single learning model.

Active Publication Date: 2017-07-21
江苏易齐物流有限公司
View PDF7 Cites 44 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It can be seen that the existing driver's driving intention recognition system has fewer classifications of driving intentions, and only considers the motion state of the vehicle and the information of the road, without considering the information of the driver itself, and the classification learning model is relatively simple.

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
  • Driving intention identification method based on improved HMM and SVM double-layer algorithm
  • Driving intention identification method based on improved HMM and SVM double-layer algorithm
  • Driving intention identification method based on improved HMM and SVM double-layer algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] The specific embodiments of the present invention will be described in conjunction with the accompanying drawings, so that future researchers can better understand the present invention.

[0030] figure 1 It is the flow chart of driver's driving intention recognition based on the improved HMM and SVM double-layer algorithm. Introduce the main idea of ​​the present invention as a whole, first collect and preprocess the data to obtain the training vector, then train the first-layer HMM classifier and the second-layer SVM classifier at the same time, and secondly eliminate the identifiable vector through the first-layer HMM classifier. The intention is to reduce the burden of the second layer SVM classifier. The training vector corresponding to the confusing intention in the first layer is imported into the second layer SVM classifier to complete the recognition of the driver's driving intention through the voting algorithm.

[0031] figure 2 Hidden Markov schematic. ...

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 driving intention identification method based on an improved HMM and SVM double-layer algorithm. The method includes: step 1, classification of driving intentions of drivers: dividing the driving intentions of the drivers into five categories: emergent left lane-changing, normal left lane-changing, lane maintaining, normal right lane-changing, and emergent right lane-changing; step 2: experiment data acquisition and processing; step 3: offline training of the improved HMM and SVM double-layer algorithm; and step 4: identification of the driving intentions of the drivers. According to the method, a human-vehicle-road system is completely considered, information of vehicles, roads and drivers is acquired, the HMM and SVM double-layer learning model is employed, and the accuracy and the timeliness of a driver lane-changing intention identification model are improved.

Description

technical field [0001] The invention relates to the technical field of vehicle intelligent driving and active safety, in particular to a driving intention recognition method based on an improved HMM and SVM double-layer algorithm. Background technique [0002] Road accidents are usually caused by some non-standard driving behaviors of drivers, and road accidents caused by lane changing process account for about 23% of the total number of traffic accidents caused by improper driver operations. Therefore, it is of great significance to reduce the occurrence of road accidents and improve the safety level of motor vehicles to carry out the research on driver's driving intention recognition through the system's complete perception of the driver's operation behavior in the process of changing lanes. [0003] At present, some advanced driver intention recognition systems have been applied in real vehicles. The current common driver's driving intention recognition system is mainly ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62B60W40/09
CPCB60W40/09G06F18/2411G06F18/295
Inventor 刘志强吴雪刚倪捷汪澎
Owner 江苏易齐物流有限公司
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