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

Driving model training method, driver identification method, device, equipment and medium

A training method and driving model technology, applied in the field of identification, can solve problems such as poor recognition effect of driving models, achieve accurate recognition and improve accuracy

Active Publication Date: 2020-11-03
PING AN TECH (SHENZHEN) CO LTD
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Embodiments of the present invention provide a driving model training method, a driver recognition method, device, equipment, and medium to solve the problem that the current driving model recognition effect is poor

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 model training method, driver identification method, device, equipment and medium
  • Driving model training method, driver identification method, device, equipment and medium
  • Driving model training method, driver identification method, device, equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] figure 1A flow chart of the driving model training method in this embodiment is shown. The driving model training method can be applied to terminal devices of insurance institutions or other institutions for training driving models so that the trained driving models can be used for recognition to achieve the effect of intelligent recognition. For example, it can be applied to the terminal equipment of the insurance institution to train the driving model corresponding to the user, so that the trained driving model can be used to identify the user who has applied for auto insurance at the insurance institution to determine whether the user is driving the car himself. Such as figure 1 As shown, the driving model training method comprises the following steps:

[0047] S11: Obtain training image data and training audio data of the same driving scene, and associate the training image data and training audio data with the user identification.

[0048] Wherein, the same driv...

Embodiment 2

[0098] Figure 6 A functional block diagram of a driving model training device corresponding to the driving model training method in Embodiment 1 is shown. Such as Figure 6 As shown, the driving model training device includes a training data acquisition module 11 , a face recognition model acquisition module 12 , an audio recognition model acquisition module 13 and an associated storage module 14 . Wherein, the implementation functions of the training data acquisition module 11, the face recognition model acquisition module 12, the audio recognition model acquisition module 13 and the associated storage module 14 correspond to the corresponding steps of the driving model training method in the embodiment one by one. Examples are not detailed one by one.

[0099] The training data acquisition module 11 is used to acquire training image data and training audio data of the same driving scene.

[0100] The face recognition model acquisition module 12 is used to train the convo...

Embodiment 3

[0120] Figure 7 A flow chart of the driver identification method in this embodiment is shown. The driver identification method can be applied to terminal equipment of insurance agencies or other agencies, so as to identify the driver's driving behavior and achieve the effect of intelligent identification. Such as Figure 7 As shown, the driver identification method includes the following steps:

[0121] S21: Obtain the image data to be recognized and the audio data to be recognized of the same driving scene of the user, and associate the image data to be recognized and the audio data to be recognized with the user identification.

[0122] Among them, the image data to be identified and the audio data to be identified refer to the real-time image data and audio data respectively collected by the user through the camera of the mobile terminal and the recording device during the actual driving process. This data is used for model identification to determine whether the user hi...

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 model training method, a driver identification method, a device, equipment and a medium. The driving model training method includes: obtaining training image data and training audio data of the same driving scene, the training image data and the training audio data are associated with user identification; using the training image data to perform convolutional neural network model training to obtain a face recognition model; using the training audio data to train the convolutional neural network model to obtain an audio recognition model; using the training image data and the audio image data to train the face recognition model and Consistency verification is performed on the audio recognition model, and the face recognition model and the audio recognition model are stored in association with the user identifier. The driving model training method utilizes the features of the image dimension and the sound dimension to solve the problem of poor recognition effect of the current driving model and improve the accuracy of identifying the driver.

Description

technical field [0001] The present invention relates to the field of identification, in particular to a driving model training method, a driver identification method, device, equipment and medium. Background technique [0002] At present, to identify whether the driver is driving himself or not, the gyroscope data obtained by the mobile phone and the mobile phone track data are generally used to judge whether the driver is driving himself, but the result of driver identification using the gyroscope data and mobile phone track data is not high. The data obtained by using gyroscope data and mobile phone trajectory data for driver identification often cannot reflect the real state of the driver's driving. The specific data used, such as the speed and acceleration of the car or the trajectory data on the map, are difficult to achieve for the driver. Accurate identification. Most of the data collected and used are the physical characteristics of the car when driving, and other c...

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 Patents(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/00
CPCG06N3/08G06V40/172G06V20/597G06N3/045
Inventor 吴壮伟金鑫张川
Owner PING AN TECH (SHENZHEN) CO LTD
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