Method for learning driving style based on self-coded regularization network

A driving style and network learning technology, applied in the field of driving recognition, can solve problems such as difficult to describe the driving style of unknown drivers

Inactive Publication Date: 2017-06-20
SHENZHEN WEITESHI TECH
View PDF8 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that existing methods are difficult to describe the driving style of unknown drivers, the purpose of the present invention is to provide a method for learning driving style based on self-encoding regularization network, using self-encoding regulari

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 learning driving style based on self-coded regularization network
  • Method for learning driving style based on self-coded regularization network
  • Method for learning driving style based on self-coded regularization network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0037] figure 1 It is a system flowchart of a method for learning driving style based on self-encoding regularization network of the present invention. Mainly including GPS data conversion, self-encoder regularization network, objective function and approximation, run-length encoding framework, driver population estimation.

[0038] Wherein, the described GPS data conversion defines the itinerary (i.e. the GPS track) by a tuple sequence (u, v, t) of varying length, where (u, v) represents the geographic location, and t represents the time, from the original GPS data To construct the neural network input, a trip is first divided into fixed length L s and press L s A window of / 2 disp...

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 learning driving styles based on a self-coded regularization network. The method mainly comprises the following steps: performing GPS (Global Positioning System) data conversion, performing regularization network self coding, performing target function sum approximation, establishing a run length encoding frame, and establishing the number of drivers, namely, in a group of unknown driving, inputting GPS data of vehicles establishing a statistic characteristic matrix as network input, introducing a marker of a limited training set as a prior into an unsupervised automatic encoder, reconstructing hidden layer RNN (Recurrent Neural Network) characteristics, extracting a neck layer of a regularization self-coding structure as a final driving style characteristic representation layer, and estimating the number of drivers in the driving process. By adopting the method, the limit that the driving style of an unknown driver is hard to describe can be solved, a self-coded regularization network is designed to directly learn driving habits of the driver from the GPS data, then recognition and classification precision of different drivers can be improved, and a relatively safe and accurate method can be provided for design of assistant and automatic driving systems.

Description

technical field [0001] The invention relates to the field of driving recognition, in particular to a method for learning driving style based on self-encoding regularization network. Background technique [0002] Driving recognition is often used in the auto insurance industry and in the fields of autopilot and assisted driving of cars to identify drivers and estimate the number of drivers. In particular, driving style information from GPS data is of particular interest to many fields, because good recognition can answer questions such as whether the driver was driving properly at the time of the accident and how many people shared the car, so reliable information can be made. Risk assessment directly affects the policy fees that insurance companies should pay. In addition, good driving style representation can help to better model and understand human driving behavior, and help improve the design of assisted and automatic driving systems. Although the existing methods can ...

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): G07C5/08G06K9/62G06N3/08
CPCG06N3/084G07C5/0841G06F18/2431
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap