Automatic driving vehicle risk assessment method and system for bicycle track prediction

A trajectory prediction and automatic driving technology, applied in prediction, biological neural network model, structured data retrieval, etc., can solve the problems of few protection measures for cyclists and high traffic accident rate, so as to improve the prediction time and prediction accuracy Effect

Pending Publication Date: 2020-12-01
UNIV OF SCI & TECH OF CHINA
View PDF3 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

High rate of related traffic acciden...

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
  • Automatic driving vehicle risk assessment method and system for bicycle track prediction
  • Automatic driving vehicle risk assessment method and system for bicycle track prediction
  • Automatic driving vehicle risk assessment method and system for bicycle track prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0064] like figure 1 As shown, this embodiment discloses a risk assessment method for autonomous vehicles based on DBN and LSTM bicycle trajectory prediction, including the following steps S1-S4:

[0065] S1. The self-vehicle device collects the information data of the movement characteristics of the cyclist at the intersection, the movement of the self-vehicle and the characteristics of the road environment;

[0066] S2. Combining the above-mentioned collected data into the graphical model of the DBN ...

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 provides an automatic driving vehicle risk assessment method and system for bicycle track prediction, and the method comprises the following steps: 1, collecting the information data ofthe motion characteristics of a rider at an intersection, the motion of a vehicle and the road environment characteristics, and obtaining the collected data; step 2, combining the acquired data into aDBN graphic model for intention inference to obtain an intention inference result; 3, according to an intention inference result, performing trajectory prediction by adopting a long short-term memorynetwork LSTM with an encoder-decoder; and 4, predicting and outputting the predicted position of the rider according to the track. According to the invention, the prediction time and the prediction accuracy can be improved; not only is motion dynamics considered, but also intention and environmental constraints of a rider are considered; the invention can improve the prediction time and prediction accuracy, and is of great significance to intelligent vehicles on a VRU protection system and a path planning module.

Description

technical field [0001] The present invention relates to the technical field of intelligent driving, in particular to a risk assessment method and system for automatic driving vehicles based on bicycle trajectory prediction. In particular, it involves a DBN and LSTM based bicycle trajectory prediction method for autonomous vehicle risk assessment. Background technique [0002] As an important part of strategic emerging industries, intelligent driving is of great significance to the promotion of national science and technology, economy, social life, and comprehensive national strength. Intelligent driving can make up for the defects of human drivers, realize intelligent driving, improve traffic efficiency, ensure safety rate, and alleviate the problem of labor shortage. At the same time, research on intelligent driving technology can enhance my country's core competitiveness in automobile-related industries, and has great strategic significance for improving my country's acad...

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): G06F16/29G06N3/04G06Q10/04G06Q10/06G06Q50/26
CPCG06F16/29G06Q10/04G06Q10/0635G06Q50/26G06N3/044G06N3/045
Inventor 高洪波朱菊萍李智军郝正源何希
Owner UNIV OF SCI & TECH OF CHINA
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