A driving behavior recognition method based on wavelet analysis

A recognition method and wavelet analysis technology, applied in the field of systematic driving behavior recognition, can solve problems such as gaps in practical application, and achieve good universality, high efficiency, and strong theoretical basis

Inactive Publication Date: 2019-01-18
重庆信络威科技有限公司
View PDF12 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with foreign research, domestic research results are less, and the practical application is even more blank

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
  • A driving behavior recognition method based on wavelet analysis
  • A driving behavior recognition method based on wavelet analysis
  • A driving behavior recognition method based on wavelet analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] In this embodiment, the ST series STM32F4 is used as the main control chip, and the LSM6DS33 is used as a 6-axis inertial sensor for data collection. The sensor integrates a 3-axis acceleration sensor and a 3-axis angular velocity sensor to meet the experimental requirements.

[0037] Specifically, in this embodiment, the sensor is placed horizontally, the X-axis points to the front of the vehicle, the Y-axis points to the left side of the vehicle body, and the Z-axis goes vertically upwards, satisfying the right-handed coordinate system. The sampling frequency is set to 50Hz, which fully meets the requirements for low-frequency signals such as driving actions. The driving behavior in this embodiment includes nine driving behaviors, namely acceleration, braking, left turn, right turn, U-turn, left lane change, right lane change, vehicle start, and vehicle flameout. For each task, 80 supervised samples were collected under real road conditions as a supervised dataset for...

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 behavior recognition method based on wavelet analysis. The driving behavior recognition method comprises the following steps: 1) decomposing the original signal basedon wavelet analysis to obtain local time-frequency domain information more accurately; 2) Based on the idea of clustering, selecting four statistical features to carry out driving behavior identification. Based on wavelet analysis, the invention expounds a more comprehensive and more accurate systematic driving behavior identification method, which has broad application prospects in the fields ofsocial safety, automobile insurance, fleet management and the like.

Description

technical field [0001] The invention belongs to the fields of signal processing, pattern recognition and machine learning, and specifically relates to a systematic driving behavior recognition method based on wavelet analysis. Background technique [0002] According to statistics from the National Bureau of Statistics of China, there were as many as 212,000 traffic accidents in 2016, and more than 63,000 people died in traffic accidents. Traffic accidents pose a great threat to life safety, but traffic accidents are mainly caused by human factors. In a study by the AAA Foundation for Traffic Safety, "aggressive driving accounts for more than half of all traffic fatalities." On the other hand, the researchers found that drivers behave more cautiously and safely when they are supervised and given feedback. Therefore, the identification of driving behavior has great social significance. Efficient and accurate identification of driving behavior can remind drivers to drive care...

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): G06K9/00G06K9/62
CPCG06V20/56G06F2218/00G06F2218/06G06F18/23
Inventor 张盛秦爽吴明林
Owner 重庆信络威科技有限公司
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